Keywords: Robotics in manufacturing, Human-Automation Integration
Abstract: The paper deals with parameters estimation in the human-robot collaboration scenario. It presents an analytical algorithm for computing of the interaction force and its application point using measurement data obtained from the internal joint torque sensors only. The proposed algorithm is based on a specific extension of static equilibrium equations allowing to find the desired interaction force and action line. Further, this general solution is combined with geometric constraints describing manipulator surfaces and corresponding friction cones. Particular attention is paid to singular cases arising when the interaction force action line intersects one or several joint sensor axes. The developed technique was carefully evaluated via the simulation study.

Keywords: Smart manufacturing systems, Robotics in manufacturing, Production planning and scheduling
Abstract: Smart factory consists of extensive automation (e.g., robotic actors) and minor human interventions. Additional characteristics are manufacturing a variety of products rapidly and accurately. Many of the products contain rigid objects assembled with deformable ones, especially deformable linear objects (DLO) such as wires. Deformable object behavior results in modifications of objects' shape and position thus influencing assembly processes. Assembly sequence planning (ASP) is an intractable issue in production management. ASP in a robotized environment such as the smart factory with objects deformations is even more challenging. Deformable objects assembly by robotic actors increase uncertainty and influence factory performances. The theory of constraints (TOC) suggests tools for improving factory performances by concentrating on production flow. The literature is lacking TOC-based tools investigating smart factories and Industry 4.0 aspects. One of TOC thinking process tools is a conflict resolution diagram. This diagram describes a dilemma influencing achieving desired goal and aids in identifying potential solutions (called "injections"). This paper describes ASP dilemma in smart factories regarding robotic manipulation with deformable objects. The dilemma between generic, unified automatic assembly on one hand and customized, designated assembly, on the other hand, is elaborated. An entity called "interaction" is introduced as an injection to the smart factory ASP dilemma. Interactions are defined based on physical, geometric, and manufacturing attributes. Defining interactions categories is an important step toward comprehensive automation of assemblies containing deformable objects. By so interactions are promising direction from both research and industrial perspectives for robotic ASP with deformable objects in smart factories.

Keywords: assembly sequence planning (ASP), deformable linear object (DLO), smart factory, theory of constraints (TOC), robotic assembly.

Keywords: Design and reconfiguration of manufacturing systems, Robotics in manufacturing, Smart manufacturing systems
Abstract: The aerospace manufacturing industry has been using dedicated assembly systems with manual processes for decades. Not only is this labour-intensive, it is also extremely wasteful as no reuse is possible at the end of a product life cycle. As automation technologies evolve, a flexible/reconfigurable assembly solution is needed to reduce tooling and manual hours in the process. However, a reconfigurable system is often perceived to be costly compared to a dedicated system. In this paper, an affordable and auto-reconfigurable assembly system is proposed for small box products. The proposed system consists of a jig frame, adjustable interface plates, reconfigurable profile boards and a pick-and-place (PnP) end effector. The jig frame can be robotically configured via a PnP process, in which the profile boards are loaded and unloaded onto the jig frame via the end effector. The process feasibility is proven with business case analyses and lab-based experiments regarding tolerance and process timing requirements. Through the business case analysis, cost-saving benefits can be achieved under realistic production scenarios, including a low-volume case. Process timing and repeatability of the jig configuration were also proven to be within the product and process requirements. The proposed tooling solution with automated robotic reconfiguration can be incorporated as a standard operation in a smart assembly factory.

Keywords: Robotics in manufacturing, Complex adaptive systems and emergent synthesis in manufacturing
Abstract: Shape control of deformable linear objects (DLOs) is an open challenge, due to the difficulty in predicting the behavior of the DLOs during the manipulation. In this paper, we propose a new approach to achieve the shape control of a DLO grasped by two robotic manipulators on a plane. The proposed approach models the DLO as a set of points. Then it derives the Jacboian matrix that maps the velocity from the robots end-effectors to the DLO points. Moreover, A strategy to maintain the DLO length constraint during the manipulation is developed to avoid excessive stretching. The new algorithm is tested in simulation environment for different desired shapes. The experiments results prove the efficiency and accuracy of the proposed algorithm to place the DLO into the new shape.

Keywords: Robotics in manufacturing, Optimisation Methods and Simulation Tools, Fuzzy logic control
Abstract: The paper deals with the problem of smooth motion of the end-effector of an overactuated cable-driven parallel robot. There is observed a way to give planar curvilinear trajectory via Fourie series. The idea of this work is to use special sigmoid function as an argument of function composition to achieve smoothness at the beginning of motion of the end-effector of a cable-driven parallel robot. The developed method makes it possible to reduce the positioning errors of the end-effector when moving along complex trajectories.

Keywords: Scheduling
Abstract: Post-disaster relief scheduling problem in sustainable humanitarian supply chains (SHSCs) has received increasing attentions from academia. Most existing works focus on the uncertain relief supplies. However, since the destruction caused by disaster can not be estimated accurately and timely, relief demands also can be uncertain. Therefore, in this paper, we study a post-disaster relief scheduling problem in SHSCs considering uncertain relief supplies and demands simultaneously. The objective is to minimize the expected total unsatisfied demand rate, adverse environment impact and economic cost on the upper level decision, and to maximize the expected total survivors' perceived satisfaction on the lower level decision. For the problem, a new stochastic bi-level optimization model is first established. And a hybrid solution approach including a sample average approximation method, a prime-dual algorithm, a linearization technique and a global criteria method is further devised. Finally, a case study is conducted.

Keywords: Scheduling, Operations Research, Optimisation Methods and Simulation Tools
Abstract: Tight deadlines, uncertain release dates and frequent disruptions impose major challenges for manufacturing. Therefore, we take a fresh look at the literature examining tardiness under unrelated machines, sequence-dependent setup times, precedence constraints, job-splitting and multiple resources. Our focus in on exact methods, including integrated methods combining Integer Programming and Constraint Programming. We also present a Bender's approach encompassing most of the above features, augmented with certain inequalities to represent additional problem features or to strengthen the problem's relaxation. We offer computational evidence and discuss possible extensions. Copyright copyright 2022 IFAC.

Keywords: Scheduling, Production planning and scheduling
Abstract: The flexible job-shop scheduling problem (FJSSP) is a well-known optimisation problem in the production scheduling literature that can be used to describe a wide variety of manufacturing environments. However, the classical definition of the problem does not allow for the description of more complex settings, where tasks may require the availability of specific resources. In this work, we extend the basic FJSSP model to support several kinds of resource constraints such as: bill of materials, utility resources, arbitrary resources and limited capacity buffers among machines. Moreover, we present a) a constraint programming (CP) model and b) a population-based CP algorithm to solve the new family of problems. The proposed algorithm uses frequencybased learning mechanisms to detect promising regions in the solution space. The extracted information is used to guide the CP towards finding high quality solutions in short computational times. We benchmark our algorithm on well-known instances of the FJSSP literature and computational results show the effectiveness of the proposed algorithm compared to a basic CP model. Comparative performance analysis on problem instances from the literature that include variants of the FJSSP with resource constraints are also presented. Lastly, an extensive analysis is performed on the effect of different resource constraints on various solution properties.

Keywords: Scheduling, Production planning and scheduling, Inventory control, production planning and scheduling
Abstract: We present a DRL-based novel approach to solve the Job Shop Scheduling Problem (JSSP) in real-time while facing unpredictable job arrival disruptions. Our proposed approach consists of continuously generating improved schedules based on a rescheduling technique: It leads to continuous generation of schedules in triggered rescheduling points, and thus gives immediate response to random job arrivals. To implement the proposed technique, we use Proximal Policy Optimization Actor and Critic (PPO-AC), a combination of two RL algorithms. PPO-AC is used to assign job operations to available machines based on the job shop state that is represented by dynamic disjunctive graphs, modeling precedence constraints between job operations, and resource sharing constraints. Graph Embedding modeling is also applied for dynamic graph representation in PPO-AC algorithm. Preliminary numerical experiments of our innovative solution are discussed in this paper.

Keywords: Supply chains and networks, Industry 4.0, Smart manufacturing systems
Abstract: In this paper, we define and conceptualize the emerging practice of “Digital Supply Chain Surveillance (DSCS)” as the proactive monitoring of digital data that allows firms to track, manage, and analyze information related to a supply chain network without needing the explicit consent of firms involved in the supply chain. After reviewing approaches to surveillance challenges that have been raised, we find that several approaches have been proposed, in particular for risk management, which have made use of Artificial Intelligence (AI) as a key enabler. By interconnecting surveillance data sources and systems, appropriate AI techniques can make surveillance easier, larger scale and possibly more informative, whilst at the same time bringing about a number of technical, ethical and managerial challenges with it. We discuss these challenges, highlighting the need to integrate multiple surveillance data and insights, the potential for hidden bias and the consequent need for AI skills to prevent bias, and the need to design guidance for embedding DSCS insights into business processes ethically, and transparently.

Keywords: Smart manufacturing systems, Supply chains and networks, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: IT project management can use both project and process-oriented approaches. Pre-investment, investment (development and implementation phase), and operations and maintenance are the three phases that make up the life cycle of an IT project. The service processes are part of the third phase. The earliest stage of an IT project's life cycle necessitates a substantial financial commitment. As a result, it may be considered a business venture. As a result, project management concepts and techniques may be used in the implementation of IT projects. During the investment phase, methods for software development and testing are implemented. It is carried out in compliance with industry standards that govern the deployment of life cycle models. The size of the program code and the complexity of the software determine the cost and timeliness of IT system implementation, maintenance, and servicing jobs. As a result, the process approach can be used to manage various phases of large-scale IT projects. Supply chain optimization, in particular, can be used advantageously. This report is devoted to the optimization of supply chains for service operations at the stage of implementation and maintenance of large IT projects. A feature of this approach is that the human resources of an IT service support team are considered as a subject for the supply chain. Conditions are also given that the supply chain for service operations considered has the resilience and viability properties. The parameters for the multiple-channel queuing model are calculated using the mathematical model presented in this paper. Microsoft Excel is used to implement this model. It enables us to generate estimations for the average time of maintenance and support jobs during the pre-investment phase, as well as determine the optimal size for the IT service support team.

Keywords: Modelling Supply Chain Dynamics, Robustness analysis, Transportation Systems
Abstract: As a result of recent pandemic restrictions, logistic systems undergo severe disruptions in the provision of requested goods. To leverage the delivery uncertainty, diverse channels may be used, leading to multi-modal distribution systems. In the paper, the (r, Q) inventory policy is formally investigated as a tool to control the resupply process of a remote depot that answers market demand, whose parameters are not known a priori and may arbitrarily change in time. The goods are shipped via multiple channels with diverse properties concerning lot size and transportation delay uncertainty, e.g., trucks, or a train line. A dynamic model of the supplier-depot interaction is constructed and used to evaluate the policy properties in a robust control framework. It is explicitly shown how to select the policy parameters to obtain full demand realization, thus eliminating backorders, irrespective of the pattern of delay and demand fluctuations. It is also discussed how to assign the warehouse space at the depot so that emergency storage will not be required even though deliveries from a few periods may arrive simultaneously, or out of order.

Keywords: Supply chains and networks, Supply Chain Management, Design and reconfiguration of manufacturing systems
Abstract: This paper discusses the challenging problem of jointly optimizing safety stock placement and supplier selection in a multi-layered distribution network, under the guaranteed service model (GSM) approach and single-supplier strategy for the customers. The original GSM-based model is extended to include optimal echelon (R, Q) policies. We propose an approximation model, which determines the optimal configuration flow of orders, order quantities, reorder points, safety stock amounts, inbound and outbound service times at each node. A two-stage heuristic is developed for this complex model that optimizes the flow configuration and then determines the other variables. Current experimental results on a few test networks indicate a promising computational time performance.

Keywords: Supply chains and networks, Supply Chain Management, Inventory control, production planning and scheduling
Abstract: Supply chains have experienced several incidents that put their performance at risk in recent years, the most recent example being the covid-19 pandemic that threatened global supply networks. Assessing the vulnerability of supply chains becomes essential, as well as developing operational and continuity plans to mitigate the consequences of disruptions and ensure the continuation of critical processes. Resilience is a strategy to reduce the vulnerability of supply chains, as it provides the ability to avoid disruption or quickly recover from it. Therefore, this research aims to propose a model that contributes to improving resilience in supply chains by integrating Industry 4.0 technologies such as Digital Twin, Internet of Things, and Blockchain that provide end-to-end supply chain visibility. Copyright © 2022 IFAC.

Keywords: Production planning and scheduling, Smart manufacturing systems, Distributed systems and multi-agents technologies
Abstract: The paper presents the architecture of a subsystem for processing, storing, visualizing and analyzing of production schedules. It is an important part of an integrated digital manufacturing process control ecosystem. The proposed system allows to process and analyze the production schedule in real time with reference to possible changes in the production situation. The system uses open source big data solutions. The results of the system operation tests with the data from production schedules of a large automotive company are presented. A methodology for updating production schedules based on the real-time predictive models for making control decisions is proposed; the overall system performance is investigated. Identification models for digital twins are offered.

Keywords: Industry 4.0, Design and reconfiguration of manufacturing systems
Abstract: Within Industry 4.0 the communication between the physical and the cyber part of manufacturing system faces an ever-growing rise in complexity. The Asset Administration Shell (AAS) is an information framework, within Industry 4.0, that describes the technological features of an asset. It was created to present data and information in a structured and semantically defined format, allowing for interoperability. The work addresses the industrial implementation of AAS, where a systematic literature review has been carried out to investigate the features of the implemented AAS metamodel, and the tools used for the realization of the models. A study of the convergence present in literature between the AAS and Digital Twin (DT) has also been carried out. This paper presents a reference of AAS tools and information for industry practitioners, as well as suggestions for research gaps in the standardization of AAS information modelling.

Keywords: Discrete event systems in manufacturing, Production planning and scheduling, Industrial and applied mathematics for production
Abstract: This research studies the generating and updating of production release plans in digital twin wafer fabs. We collect the manufacturing data of the workload and the expected output for each planning period from the manufacturing execution system and the data monitoring system. Then, we fit the above data as piecewise linear clearing functions (CFs) and import the parameters of clearing functions to the mathematical planning model. A theoretical optimal solution can be calculated by the mathematical model. We import this solution as an initial solution into the discrete-event simulation model to perform simulation iterative optimization, until a satisfactory solution is obtained. To reflect the physical production system data changes in time and to update production plans, we design two update strategies. Strategy 1: The discrete event simulation model in virtual space is updated when key parameters exceed the designed thresholds. Strategy 2: The simulated data used by clearing functions is updated after every production planning process. We fit clearing functions using both historical data and simulation prediction data.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Industry 4.0
Abstract: Digital twins (DT) constitute a major concept of future industrial systems. They are expected to enable efficient virtualization of manufacturing systems and enhance various decision-making processes. In parallel, many initiatives exhibited how artificial intelligence (AI) could increase the performance of the DT on specific applications. By reviewing the literature combining AI and DT, a lack of contributions on the whole life cycle of the DT was exhibited. Therefore, the main contribution of this paper is to define a global integration framework of AI into DT, focused on the exploitation phase of the DT. A case study, using a relatively simple physical twin, illustrates the potential of such integration for the response of the DT to unpredictable modifications of the physical twin.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Sustainable Manufacturing, Industry 4.0
Abstract: Based on relevant studies, demanufacturing processes on end-of-life products may achieve around 70% material-saving, 60% energy-saving, and 50% cost-saving of the overall life-cycle cost. Despite these great benefits, many issues are still open in real-life applications, such as the unavailability of smart technology implementations that make more effective and efficient demanufacturing processes. The Digital twin application seems to be a promising tool to facilitate recycling, tracking, and managing such processes. This paper reviews the state-of-the-art research on digital twins for manufacturing and demanufacturing. A comprehensive analysis of the key challenges in applying digital twins for the demanufacturing process and potential solutions for the current challenges are provided.

Keywords: Scheduling, Smart transportation
Abstract: The volume of freight transportation, including multimodal (using rail and water, road or air transport), is increasing every year. International multimodal transport by rail and sea is an important part of integrated logistics services and forms the basis of current global cargo turnover. Sea transport holds the first place in the total number of freight shipments of international transportation. Rail transport takes more than 87% of domestic freight traffic and is increasing annually. Complex logistic service is one of Russian Railways holding services. In particular, the company deals with scheduling in international multimodal transport. Sea port-railway transhipment points have a key role in the realization of such transportation. This paper considers the complex problem of unloading the arriving vessels and the formation of trains with the objective function of minimizing the total weighted delivery time of cargo to the destination point and minimizing the cost of forming trains. In a general sense, the formulation can be given as follows. A port and its berths are known. We are given a planning horizon, during which we have to define a berth for unloading for all incoming vessels. Each berth can unload only certain types of cargo. A port consists of several operational facilities: berths (seaside), where vessels are unloaded (or loaded); a cargo terminal (yard), which is a buffer area for cargo waiting for further transportation; a loading terminal (landside), where cargo is loaded on land transport and sent to destinations. After unloading the vessels, all cargo has to be assigned to trains for delivery to the destinations.

Keywords: Scheduling, Optimization and Control, Operations Research
Abstract: Short-term detailed multiproduct pipeline scheduling is a complex problem with many industry-specific constraints. We are interested in solving an operational scheduling problem of a straight pipeline in the phosphate industry. In this article, we propose a mixed-integer linear programming model (MILP) model with a discrete-time formulation to provide a detailed scheduling solution for a storage-sensitive problem. The objective is to determine the charging and discharging schedule of the slurry pipeline, to satisfy products demand while respecting all pipeline and storage constraints. The model was tested on different instances, proving to be able to minimize products sold-outs and the total water quantity scheduled.

Keywords: Production planning and scheduling, Optimization and Control, Scheduling
Abstract: The problem of planning the work of operating departments with scheduled admissions of patients is investigated. Two models are constructed: with and without anesthesiologists. Approximate algorithms were developed for each model. All experiments were performed on real, high-dimensional data. The resulting solutions satisfy the hospital’s requirements for accuracy and speed.

Keywords: Optimisation Methods and Simulation Tools, Optimization and Control, Heuristic and Metaheuristics
Abstract: In this article, a new practical problem is proposed, as well as algorithms for solving the one-dimensional bin packing problem. The generalization of this problem is one of the most fundamental problems of combinatorial optimization and has been widely studied for decades. Our formulation for this problem takes into account not only the different weights of products and separability but the difference in their types. An objective function is formulated that minimizes the sum of the components with weights responsible for different characteristics of product distribution. Parameter generation of the problem is based on data that approximate the real one. In order to compare the algorithms 168 test instances were generated. In addition they were solved optimally using the Gurobi solver with a time limit of 1 hour. The algorithms proposed are based on the separation of the set of subjects under consideration on the basis of divisibility. Also, the proposed algorithms have been tested on a large family of generated instances with the number of bins from 100 to 1000 ones. Copyright © 2022 IFAC.

Keywords: Optimisation Methods and Simulation Tools, Scheduling, Enterprise modelling, integration and networking
Abstract: Optimization of large-scale discrete-time scheduling problems is challenging due to the combinatorial complexity of binary or discrete decisions to be made. When including networks of unit-operations and inventory-tanks to fulfill both the logistics and quality balances as found in complex-scope process industries, the decomposition of mixed-integer nonlinear programming (MINLP) regarding its quantity-logic-quality phenomena (QLQP) paradigm into mixed-integer linear programming (MILP) and nonlinear programming (NLP) has been commonly and naturally used to find solutions of industrial-sized problems. Other approaches can be incorporated into an optimization-based decision-making framework to provide proper capabilities for handling complex large-scale applications. This includes strategies related to reduction of model, time, and scope that can be based on machine learning approaches and heuristic algorithms. Such a decision-making framework is useful not only to allow solving industrial-scale problems, but also to achieve enhanced applications. There are open challenges to automatically solve complex large-scale discrete-time problems in acceptable computing time. In this context, this paper employs a decision-making framework based on modeling and optimization capabilities to handle large-scale scheduling problems. The examples are built using the unit-operation-port-state superstructure (UOPSS) constructs and the semantics of the QLQP concepts in a discrete-time formulation. The proposed framework is shown to effectively use decomposition and heuristic strategies for solving industrial-sized scheduling formulations.

Keywords: Production planning and scheduling, Operations Research, Robustness analysis
Abstract: This paper presents a new formulation of the flexible job-shop scheduling Problem with outsourcing options adapted for Engineer-To-Order (ETO) products. Our formulation enables modeling complex product structures with flexible precedence relations between elements and operations. Having a significant role in the ETO context, the specificity of non-physical operations (design and engineering) is taken into account. Indeed, non-physical operations are subject to a validation stage, can be iterated in case of non-validation, and are executed once for several identical elements. The proposed approach is governed by a new ETO strategy to overcome the impact of the design uncertainty and element cancellations (time and financial wastes). First, the production and purchase of the most uncertain elements are delayed at the latest while their design is validated early. Besides, in the presence of similar elements, an element can be saved when cancelled by being used as a sub-part of another one. The proposed approach sequentially solves two mathematical models. The first model aims to minimise the makespan and the projects costs. The second model maximizes the solution robustness and the ability of saving elements while being governed by the completion time and project cost, output of the first model. The obtained results and a comparative study show the efficiency and robustness of the proposal.

Keywords: Production planning and scheduling, Optimisation Methods and Simulation Tools, Heuristic and Metaheuristics
Abstract: Most industrial planning and scheduling problems are NP-hard, stochastic, and subject to multi-objective. A wide variety of heuristic methods have been designed or adapted to solve them. However, the Genetic Algorithms (GA) family is both the most used and one of the most efficient for several well-known problems. This paper reviews GAs proposed in the literature, focusing on the techniques to overcome scheduling challenges (cycle avoidance and feasibility). This paper also has a didactic purpose and details modern approaches to reach high-quality solutions: self-adaptation, learning process, diversity-maintenance, parallel computation, multi-objective, and hybridization. These mechanisms are also essential to integrate the method in current IT systems.

Keywords: Scheduling, Heuristic and Metaheuristics, Optimisation Methods and Simulation Tools
Abstract: Motivated by parallel machines scheduling in practice that receive unplanned urgent job under uncertain environment, and it usually requires a response as soon as possible since its high-priority. This paper considers the response time to urgent job in a worst-case as an evaluation indicator. To the best of our knowledge, this paper is the first to study a parallel identical machine scheduling problem, which is to minimize the largest waiting time of an urgent job with the uncertain processing time of regular jobs. The objective of this problem is depicted to minimize the inter-completion time, that is, the maximum difference of the completion times between any two consecutively completed jobs. We first establish a stochastic programming model, and propose a scenario-reduction based sample average approximation method to solve this uncertain problem.

Keywords: Decision Support System, Scheduling, Smart manufacturing systems
Abstract: Machine Learning (ML) practice represents a vital construct for developing intelligent Cyber-Physical Production Systems (CPPS) capable of making timely optimization for Maintenance and Planning actions. Integrating Adaptive Production Planning and Prescriptive Maintenance (PsM) in future factories provides a novel perspective for flexibility, customization, and resilience of production plans. To this end, we propose a framework for developing an intelligent Decision Support Agent (DSA) for integrated PsM and production planning and control (PPC) based on Reinforcement Learning. The paper highlights the practical implications of developing an autonomous DSA from an ML perspective using a demonstrative use case of integrated Maintenance and PPC.

Keywords: Inventory control, production planning and scheduling, Smart manufacturing systems, Industry 4.0
Abstract: In order to achieve on time delivery and win in competition, the Protection Mechanism of on time delivery with Smart Production Control System in Industry 4.0 is proposed in this paper from the perspective of process control. The Smart Production Control System is set up for Protection Mechanism with the shortest transmission path and the strategy of endogenous security by minimum device cost in IoT. The statistical techniques of the Measurement System Analysis, Statistical Process Control and Hypothesis Testing are utilized to analyze the real-time data and monitor the on time delivery in the Smart Production Control System. The evaluation shows the Protection Mechanism has process capability of Cp>1.33 and Cpk>1 with output increased 51% and days saved 33% for on time delivery, which demonstrates that the Protection Mechanism can ensure on time delivery and achieve business goal in supply chain management of Industry 4.0.

Keywords: Scheduling, Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: In this paper we investigate how decentralised scheduling approaches can be used to improve manufacturing scheduling. In view of the potential shown by some of these novel decentralised approaches, we conduct a series of experiments on a set of job shop instances subject to different degrees of variability in their processing times, and compare the performance of different scoring methods under the Contract Net Protocol proposed by Guizzi et al. (2019) with the objective of minimizing the expected makespan. We also compare the performance of the optimal (centralised and deterministic) solution in the stochastic setting, as well as a hybrid centralised-decentralised approach. Despite some limitations in the experiments, the results show the excellent performance of the decentralised approach if its operating parameters are optimized, and that the hybrid approach serves to overcome some of the problems of both centralised and decentralised approaches.

Keywords: Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes, Quality management
Abstract: “Data is the new oil” is a frequently pronounced statement. It is expected that intelligent utilization of information will change economies. With respect to production systems this fact can become reality by utilizing the Industry 4.0 Asset Administration Shell. Within this paper ways to fill and exploit the data treasure with respect to production system engineering data will be described highlighting the importance of multi-modelling of production systems.

Keywords: Production planning and scheduling, Inventory control, production planning and scheduling, Production Control, Control Systems
Abstract: Manufacturing companies are under constant pressure to increase efficiency and to achieve logistical objectives. Improving production planning and control (PPC) has significant impact on these efforts. At the same time, increasing complexity and dynamics of PPC environments make PPC more difficult. One way to cope with this situation is the application of machine learning (ML) methods. In this article, we therefore address the current state of PPC-ML research and show, based on the Aachen PPC model, in which PPC tasks and subtasks ML is already applied and to what degree the task is covered by ML. The analysis is limited to core and cross-sectional tasks of the Aachen PPC model, procurement and network tasks are not included. Furthermore, a broad analysis of the targeted data mining, business and logistic objectives is conducted. In addition, we also identify motivations which prompted researchers to apply ML in PPC.

Keywords: Scheduling, Modeling, simulation, control and monitoring of manufacturing processes, Industry 4.0
Abstract: The current production context is characterized by a complex and dynamic environment in which flexibility is a fundamental requirement for remaining competitive and meeting the needs of the new increasingly customized demand. Being flexible entails making quick decisions and re-scheduling production, in order to efficiently respond to the variability of the demand. Traditionally, scheduling deals with optimal allocation of the activities that must be carried out with the available resources, based on chosen criteria. However, it must take into account external constraints that may limit the rate to which production performance can be completely optimized: the deadlines. As a result, a dispatching rule capable of evaluating the resource availability and optimizing production performance while adhering to the occurred due-dates constraints is needed. This paper aims to propose a new dispatching rule able to assign the jobs to the available resource by considering the processing time, the machine's utilisation and the due dates, evaluating the advantages of an Industry 4.0 enabled Job Shop production system. The system’s performances are analyzed through a simulation-based approach with a highly parameterizable and modular model, adapted from the literature. The proposed dispatching rule's performance is then compared to a traditional First-In-First-Out (FIFO) and an Earliest Due Date (EDD) dispatching rule. The findings showed how the proposed dispatching rule resulted in interesting considerations about the performance of the production system.

Keywords: Design and reconfiguration of manufacturing systems, Smart manufacturing systems, Decision-support for human operators
Abstract: Planners are supported in the fulfilment of their tasks by so-called PPC systems. Among other things, planners are dissatisfied with the way PPC systems present information and results. As a result, there is a risk that the number of errors due to misinterpretations will increase. These are primarily caused by Cognitive Biases. Even increasing automation will not change this, since according to experts, humans will remain central, especially in decision-making. The European Union points out that Industry 4.0 focuses more on technical feasibility and implementation and less on human needs. In response, it proposes the human-centered approach of Industry 5.0. Here, the human being should move to the center of the process. In this regard, there is still little research activity in the field of PPC and in the context of Cognitive Biases. Therefore, we want to take a first step towards the human-centered approach with this work. To this end, we conduct a systematic literature review and develop a questionnaire to determine the human-centered maturity level. The results will then be compared with already researched Cognitive Biases.

Keywords: Smart manufacturing systems, Robotics in manufacturing, Industry 4.0
Abstract: In the research project ROBxTASK a platform is being developed that allows users to define robotic workflows using the visual programming language Google Blockly. Users can create custom agent code to call the skills of the robotic systems, send/receive messages between agents and thus orchestrate workflows for multiple agents. To meet these requirements, a simple runtime environment (RTE) is developed in Python to enable the integration of various robot infrastructures into an overall architecture. This RTE provides communication between agents, simple service discovery and dispatching, testability without hardware, logging and monitoring of processes, and requires only an MQTT broker as minimal infrastructure. Using an industrial part delivery use case, we show how this simple RTE can be used to develop autonomous, self-mediated, collaborative robotic workflows, as well as to integrate human operators into decision-making processes.

Keywords: Robotics in manufacturing, Optimization and Control, Robustness analysis
Abstract: In this paper, a discrete-time sliding mode controller (DTSMC) is designed for full position and attitude control of a quadrotor UAV. The aim of this study is to design a controller suitable for practical implementation on an autonomous quadrotor for remote sensing in the hostile nuclear environments. A nested DTSMC is developed and compared against two continuous-time sliding mode control methods; classical SMC as well as a chattering-free SMC (CFSMC) studied in the previous works. The performance of the controllers are evaluated in combination with the Hector SLAM algorithm for localisation in GPS denied environments. For this purpose, MATLAB in combination with the Robotic Operating System (ROS)are used to develop the controllers. Control signals are sent from MATLAB to the Gazebo simulation environment in ROS, which simulates the quadrotor and runs the Hector SLAM algorithm.

Keywords: Robotics in manufacturing, Complex adaptive systems and emergent synthesis in manufacturing, Optimization and Control
Abstract: This paper studies leader-following synchronization control of a group of multiple quadrotor unmanned aerial systems (UASs). A robust distributed scheme is developed to maintain the attitude motions of UASs with an active virtual leader. Complicated settings are considered in the design, where the topology is in a directed graph, and only one or some agents are connected to the leader. UASs can have different dynamic parameters. Also, some time-varying disturbances are added to the closed-loop system. A control protocol containing a robust term is proposed to each UAS to achieve asymptotic consensus. A rigorous mathematical proof and numerical example are presented to demonstrate the effectiveness of our scheme.

Keywords: Robotics in manufacturing, Optimization and Control, Robustness analysis
Abstract: Suspension system is one of the most effective vehicle components that play an essential role in the stability and comfort of the vehicle. The passive suspension can not fully meet a car's stability and comfort requirements. Instead, an active suspension system has been proposed to improve these challenges. Active suspension minimizes the vibrations entering the body using a closed-loop control system. To this end, in this research, an integral terminal sliding mode control (integral TSMC) for an active nonlinear car suspension system under external disturbances and uncertainties is designed. First, the integral TSMC is designed to deal with the uncertainties and the external disturbances in the system when the upper bound is known. Next, an adaptation law is recommended to estimate the upper bound of uncertainties and external disturbances. The results show that the proposed integral TSMC improves the convergence rate and tracking error of the closed-loop system. The stability of the nonlinear control system is investigated and proven using Lyapunov's stability theory. The numerical results indicate a good robust performance and stability for the proposed controller for the nonlinear suspension system with different road profiles in the presence of uncertainties and external disturbances. From the results, it can also be understood that important measures such as ride comfort, road holding, and mechanical structural limitations are met using the proposed approach.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control, Industry 4.0
Abstract: In the framework of the Fourth Industrial Revolution, the manufacturing industry has been immersed in a digitalization process leading to constantly increasing available data at the production line thanks to novel sensors, Cyber-Physical Systems, and the Industrial Internet of Things. These newly available process data streams can be leveraged for real-time planning, control, and process optimization towards a more efficient and competitive manufacturing paradigm. The metal forming process is a manufacturing process used in a wide variety of complex industrial pieces and products: from automotive parts such as car body panels to furniture, electronics, etc. Ensuring high quality and robust materials while optimizing production of parts is an important industrial challenge. Nowadays, batch and cycle time optimization rely on experience and expert knowledge. In this work, we propose a dynamic programming approach for real-time decision-making in hot metal forming production to minimize batch and cycle time. This paper introduces a successful proof of concept, and the first step for an autonomous self-learning control system in stamping processes, demonstrating its capabilities for overall batch and cycle time process optimization while maximizing product quality.

Keywords: Inventory control, production planning and scheduling, Optimisation Methods and Simulation Tools, Supply Chain Management
Abstract: This paper provides insights into the management of perishable products from a simulation-inventory modeling perspective. By studying a just-in-time (JIT) inventory system under a single-setup multiple-delivery setting, we propose a new simulation-optimization approach for analyzing JIT purchasing agreements between a single supplier and a single retailer when dealing with randomness. Our findings show that using deterministic inventory models in a stochastic environment can be particularly useful for modeling and optimizing complex inventory systems, i.e., when difficulties arise in characterizing randomness through an analytical model.

Keywords: Inventory control, production planning and scheduling, Optimisation Methods and Simulation Tools
Abstract: The need for optimal inventory control strategies for perishable items is of the utmost importance to reduce the large share of food products that expire before consumption and to achieve responsible food stocking policies. Our study allows for a multi-item setting with substitution between similar goods, deterministic deterioration, delivery lead times and seasonality. Namely, we model demand by a linear discrete choice model to represent a vertical differentiation between products. The verticality assumption is further applied in a novel way within product categories. Specifically, the same product typology is vertically decomposed according to the age of the single stock-keeping unit in a quality-based manner. We compare two different policies to select the daily size of the orders for each product. On the one hand, we apply one of the most classical approaches in inventory management, relying on the Order-Up-To policy, modified to deal with the seasonality. On the other hand, we operate a state-of-the-art actor-critic technique: Soft Actor-Critic (SAC). Although similar in terms of performance, the two policies show diverse replenishment patterns, handling products differently.

Keywords: Production Control, Control Systems, Production planning and scheduling, Inventory control, production planning and scheduling
Abstract: Manufacturing systems are often subject to dynamic market conditions, characterized by demand variations over time. Production policy optimization in this situation is more challenging than in case of stationary demand rate. When manufactured products are perishable, the demand variations are of particular importance, as they often result in additional losses due to disposal of perished products. In particular, that is the case in food and pharmaceutical industry. Both these aspect must be taken into account for production policy optimization. It is particularly important when the production facility is failure-prone. The rationale here is that conventional approach is based on setting the safety (hedging) inventory level in order to cope with potential equipment failures leading to shortage. For perishable products, however, this approach needs revision due to eventual deterioration of products kept in stock longer than the shelf-life limit. To address the production control problem in this context, a 3-steps procedure is developed. First, the hedging inventory level that varies in time adapting to demand variations is computed. Second, the upper limit for the products kept in stock (perishable inventory limit), which depends on the shelf-life and demand variation pattern (thus also varies in time) is determined. Third, that perishable inventory limit is shown to determine an upper bound for the hedging level. The proposed production policy adapts to demand variations and accounts product shelf-live; it is optimal and results in no perished products. Copyright © 2022 IFAC.

Keywords: Inventory control, production planning and scheduling, Heuristic and Metaheuristics
Abstract: In this paper, we consider a single retailer who sells multiple products subject to decay, and that implements coordinated inventory replenishments among them. The overall available surface at the retailer is limited and is partitioned into two areas: the backroom facility and the display area. The demand rate of each product is a function of displayed quantity and location, and it also depends on the cross-elasticity among items. The objective is to find the product assortment, the replenishment policy of each product, the quantity to be displayed, and the surfaces assigned to the backroom and display areas that maximize the total profit per time unit. We develop the mathematical model and formulate the optimization problem. Finally, we investigate the model response by means of numerical experiments considering several problem instances.

Keywords: Modelling Supply Chain Dynamics, Transportation Systems, Facility planning and materials handling
Abstract: Intercontinental perishable cargo is shipped mostly in refrigerated (reefer) containers, specialized for perishable goods. Specific conditions should be provided and controlled properly due to the limited shelf life of such cargo and the transport must last as short as possible. The article discusses the advantages of locating the port of Cartagena for this type of transport, as shorter transport reduces the likelihood of perishable goods reaching the buyer in a worse condition than stipulated in the contract between sellers and buyers. The gravity model estimates possibilities to open a new port in Murcia Region to reduce the total transportation time, including the waiting time of the vessels in Mediterranean ports. The gravity model which includes the perishability dynamics is embedded in a hierarchical network of the perishable cargo transport, where the timing of transport plays an important role. This approach was used to evaluate possibilities to build new container terminals in the Cartagena area. This approach could also improve the results of the evaluation of location advantages of ports for other intercontinental perishable cargo.

Keywords: Decision Support System, Design and reconfiguration of manufacturing systems, Distributed systems and multi-agents technologies
Abstract: Forming collaborative assembly along supply chains is a challenge in the presence of resource limitations. This paper presents an Ontology-driven Assembly Design Method (OADM) to facilitate the design of demand-driven SME collaborations envisioned in digital transformation initiatives such as Industry 4.0. The Relevance cycle addresses the key requirement to reducing coordination costs of such SME collaborations. The Design cycle develops the ontology, which includes: (i) goals, supplier processes, and resources as hierarchies of classes; (ii) properties to interconnect these classes; and (iii) SWRL rules to derive a possible combination of process steps to reach the goal of collaborative assembly. The feedback from SME Cluster managers indicates that OADM is a promising approach to overcoming barriers to planning demand-driven SME collaborations. The Rigor cycle explains its contribution to collaborative industrial engineering.

Keywords: Modelling Supply Chain Dynamics, Optimization and Control, Optimisation Methods and Simulation Tools
Abstract: Optimal control is a convenient way to develop both supply chain process optimization models and describe the dynamics of process fulfillment. A rich diversity of knowledge has been developed for the integration of optimization and simulation methods with applications to supply chain management at conceptual, informational, and computational levels. At the same time, model-algorithmic integration and alignment frameworks have received less attention. The importance of this level should not be underestimated since synthesis and analysis problems in supply chains imply tight intersections between and within the models (e.g., objective functions and constraint systems). This paper seeks to bring the discussion for-ward by carefully elaborating on the issues of optimization and simulation model and algorithm integra-tion and providing implementation guidance. Conventionally, optimization has predominantly been used at the planning level while dynamic system control was frequently investigated using simulation models. This study develops an integrated optimization-simulation framework at the model-algorithmic level for the given domain. We offer insights on how to describe planning and control in a unified model-algorithmic complex with consideration of uncertainty factors which are anticipated at the planning and confronted at the control stages. The developed theoretical framework was exemplified by a combined optimization-simulation modelling of the SC design and planning problem with disruption risks consideration in anyLogistix.

Keywords: Design and reconfiguration of manufacturing systems, Knowledge management in production, Decision Support System
Abstract: : Today, aerospace products need to be developed following economic and environmental drivers. Aerospace production systems must adapt to novel products that reuse current industrial resources to accommodate new scenarios. Reconfigurable Production Systems (RPS) or Reconfigurable Manufacturing Systems (RMS) is the answer. This paper aims to perform a preliminary review of the literature to answer the following research question. What is the current application of Reconfigurable Manufacturing Systems in the aerospace industry? This preliminary literature review is focused on the role of industrial resources in the design of RMS at the conceptual stage and the application or applicability of RMS in the aerospace industry. The objective of this paper is to provide the research community with a preliminary review of research trends in the field of industrial resources and RMS systems in aerospace.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Production Control, Control Systems, Knowledge management in production
Abstract: This paper describes a methodology for developing a digital twin (DT) based on a rich semantic model and principles of system engineering. The aim is to provide a general model of digital twins (DT) that can improve decision making based on semantic reasoning on real-time system monitoring. The methodology has been tested on a laboratory pilot plant that acts as a material handling system. The key contribution of this research is to propose a generic information model for DT using foundational ontology and principles of systems engineering. The efficacy of the proposed methodology is demonstrated by the automatic detection of a component level failure using semantic reasoning.

Keywords: Inventory control, production planning and scheduling, Operations Research
Abstract: This paper proposes two multi-period, multi-item capacitated stochastic lot-sizing problems under demand uncertainty. We model uncertainty via a scenario tree. The first model considers production, inventory, backlogging, line status, and worker group assignment decisions, where inventory and backlogging decisions have wait-and-see structure. The second model converts line status and worker group assignment decisions to the wait-and-see structure. Also, the second model enables us to take corrective extra-ordering decisions using scenario-based wait-and-see decisions. Numerical results compare the optimality and CPU time performances of two models and solution approaches using a data set inspired by a real-life electronics company.

Keywords: Inventory control, production planning and scheduling, Supply Chain Management, Probabilistic & statistical models in industrial plant control
Abstract: The inventory management of medical items in humanitarian operations is a challenging task due to the intermittent nature of their demand and long replenishment lead-times. While effective response to emergency results in inventory build-up which saves human lives, excess inventories could be intentionally burnt or donated which is costly for humanitarian organizations. Henceforth, linking demand forecasting to the inventory control task is shown to be a significant scope to offer a higher performance. In this vein, it is key to accurately select adequate forecasting methods. This paper investigates the effectiveness of parametric and non-parametric demand forecasting methods that are commonly considered to deal with stock keeping units (SKUs) characterized with an intermittent demand in industrial contexts. To do so, we conduct an empirical study by means of data related to 1254 SKUs managed in three warehouses of a major humanitarian organization based in Geneva, Middle-east and Africa. The investigation is carried out to compare the inventory performance of three parametric and two bootstrapping methods when used with an order-up-to-level inventory control policy. The results demonstrate the high performance of the bootstrapping methods in achieving higher service levels. The investigation enables to gain insights on the forecasting method that should be selected under particular assumptions on the demand and the lead-time value.

Keywords: Decision Support System, Supply Chain Management, Industry 4.0
Abstract: The management of perishable food inventory demands special attention. Fruits quickly lose their freshness and perish if they are not consumed within a specified period. It is critical to develop a management tool based on the Internet of Things that can efficiently integrate all the dynamic data associated with various types of resources in real-time along the supply chain. This research is part of a comprehensive supply chain framework developed to analyze food bank logistics supply chain interactions. The study will mainly focus on the use of historical time-series data to create a digital twin that can anticipate future events. The digital twin framework was built based on the operational trend of the Italian food bank to strengthen the decision support system related to the fresh food inventory. The SAP Analytics Cloud was used to create a solution that would help the organization better satisfy consumer needs by reducing fruit waste in the inventory.

Keywords: Inventory control, production planning and scheduling, Supply chains and networks, Decision Support System
Abstract: The majority of publications in the scientific literature investigate stochastic demand processes with constant order lead times. In practice, inventory management software like MRP is used by companies even if ignored the uncertainty of lead times. In this work, we investigate the influence of this type of uncertainty and existing solutions proposed in the scientific literature. To do this, ARTIREV tool is used to identify the major schools of thought competing in the filed and highlight the current and emerging research themes. One conclusion is obvious from this preliminary work. Even though lead times are very disruptive to supply chain, as seen during the Covid crisis, most of the work is still only interested in the uncertainties related to demand.

Keywords: Inventory control, production planning and scheduling, Production Control, Control Systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Inventory control in the frame of supply chain systems represents a challenging problem due to their dynamical behavior, ranging from fast changing demand, transport and delivery delays and production phenomena like bullwhip effect. Nevertheless, modeling such systems in order to capture all its components and to design a robust and dynamic management, remains an open problem. In this paper, a model-free control which permits to design several control strategies without need to any exact model, is proposed. Several numerical simulations as well as the conducted comparative studies show the relevance of the proposed approach. In addition, the paper considers the case of a poor knowledge of the supply chain model and introduce an exact and fast deterministic method which is of a algebraic flavor in order to estimate the parameters of the model.

Keywords: Inventory control, production planning and scheduling
Abstract: This work presents an extension of the classical newsvendor model that considers the inventory costs more accurately based on the actual stock-level within the selling period, and not on the stock-level at the end of it. The new feature of this model is that the selling period, which is relatively long, is comprised of n epochs, where the demand distribution in each epoch is known but is not stationary, and holding costs are considered only for the epochs in which the item was stored. A mathematical model is developed to calculate the expected profit, and an optimality equation is provided from which the optimal order quantity can be derived. Using a numerical analysis in a factorial experimental design of a non-homogeneous Poisson demand process, we evaluate the performance of the suggested model in comparison to two approximated standard newsvendor models that disregard the exact inventory costs.

Keywords: Inventory control, production planning and scheduling, Supply Chain Management, Operations Research
Abstract: In recent years, the problem of establishing the inventory levels in the manufacturing industry has considerably worsened due to the accelerated growth in demand for customized products, this behavior in the customers has created heterogeneous and unstable demands too difficult to forecast generating an increase in inventory levels. In response to this behavior, the strategy that companies have commonly used to offer a wide variety of products to meet this demand is Mass Customization. However, this response has created products with shorter life cycles significantly increasing the risks and costs in inventories due to deterioration, re-manufacturing, and obsolescence. Little attention has been paid to determining inventory levels when a company uses mass customization as its main production strategy. This paper develops two bi-objective optimization models to determine the optimal inventory levels for those industries that, due to the nature of the product, use mass customization to meet the needs of their customers. The performance of the models is compared with the performance offered by the traditional continuous review (Q, R) policy, the results show a significant reduction in total inventory costs while maintaining high levels of customer service level.

Keywords: Supply Chain Management
Abstract: Industry 4.0 and digitalization increasingly requires managers to make decisions in shorter time frames and at a higher level of accuracy. Among the many decision-making tools to help managers are multi-criteria decision-making methodologies (MCDM) such as Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). The aim of this paper is to develop a MCDM hierarchical model with specific attributes of AHP/ANP to aid the decision-making process of buying capital spare parts from different suppliers. The paper is based on a case study of the comparative application of AHP and ANP approaches in a manufacturing company that develops and produces parts. We consider the decision involving 3 suppliers of a capital spare part (SP) with 3 decision makers inherent in the process. For this model, 6 attributes were grouped into 2 hybrid criteria. The results obtained through the decision process using the AHP and ANP are then compared and discussed.

Keywords: Human-Automation Integration, Line Design and Balancing, Industry 4.0
Abstract: Assembly line balancing problems have been the subject of numerous studies for decades. The recent advantages of technologies in the Industry 4.0 era and integrating collaborative robots into the assembly systems have created task allocation, workload balance, and scheduling challenges. This paper provides a brief literature review on the existing collaborative assembly line balancing problems comparing their main characteristics and suggests some research directions for future studies.

Keywords: Robotics in manufacturing
Abstract: The paper deals with elastostatic calibration of industrial robots. We compared three identification strategies - namely 6 DoF, 4-6 DoF after 3 DoF, 4-6 DoF after 6 DoF. The comparison is based upon the conventional measures of robot position and on the measures of robot arm positions. Here we present the analysis of model production techniques and dataset filtering and fusion methods. All hypotheses were tested on the real experimental data collected with absolute measurement system. The results showed that last joint parameter can hardly be estimated from real experimental data and accuracy analysis can be done separately for z-direction and in-plane deflections. The identified elastostatic model parameter allowed to completely compensate the deflections in z-direction with deviation 0.3mm or 80% of entire positioning error.

Keywords: Robotics in manufacturing, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: The paper show a state-of-the-art approach for programming industrial robotics manipulator via mixed reality holographic interface. In mixed reality, we implement basic functionality for robot programming: setting up the base and tool frames, setting the sequence of Cartesian poses and Joint positions for robot motion, robot movements simulation. The software developed is based on Unity, ROS, and MoveIt frameworks. We compared cursor-based interface (Microsoft HoloLens 1) and full hand tracking (Microsoft HoloLens 2 and Oculus Quest 2) functionalities for interactive robot programming. We tested our approach on the UR10e collaborative robot in the virtual scene and real environment. The full hand tracking human-robot interaction approach improves the setup of Cartesian and Joints robot goals in respect to cursor based interaction.

Keywords: Robotics in manufacturing
Abstract: This paper deals with the modelling and control of a hinge integrated in a luxury product. In this context, following the works of Boyer and Belkhiri (2014) on modelling of multibody systems, an application of Newton-euler algorithms and space reduction processes to a mechanism with closed loops is proposed. Especially, a focus to a special linkage namely ”cam and follower” mechanism which is a closed loop whose the contact point moves along a curve carried by the bodies, is done. Regarding the integration, a dynamic controller, i.e a computed torque algorithm, has been implemented in a mock-up of the opening system under consideration. The solution allowed us to control, with the expected performance, the opening and closing of a lid.

Keywords: Robotics in manufacturing, Complex adaptive systems and emergent synthesis in manufacturing, Optimization and Control
Abstract: This paper deals with computer-controlled pneumatic actuators for the Gough-Stewart platform with 6 degrees of freedom. An important feature considered in the paper pneumatic actuator is the use of a group of valves rather than a spool valve to control the pressure in the chambers, which leads to a significant quantization in terms of the level of the control action. In addition, there is a sampling in time in the control loop. The present work is aimed at ensuring the robustness of the pneumatic actuators for the Stewart platforms concerning their parameters and the effect of changing the load on the drive caused by the interdependence of the drives due to the movement of the platform. Two discrete-time (parametric, and signal-parametric) adaptive control algorithms, based on the concept of an implicit reference model with anti-windup correction are proposed. The simulation results are presented, demonstrating the efficiency of the signal-parametric algorithm.

Keywords: Inventory control, production planning and scheduling, Smart manufacturing systems, Scheduling
Abstract: We simultaneously update multiple performance measures of manufacturing systems after several scheduling perturbations through a single pass without calculating from scratch. Our approach reuses information from the schedule before the perturbations. It utilizes Max-Plus algebra and system matrix to calculate changes propagated from initially perturbed operations to their affected successors. Then, by using these changes, it evaluates new measures for affected operations. Previous algorithms either need to calculate multiple performance measures from scratch by disregarding information before perturbations; or calculate only a single performance measure at a time while reusing system information before perturbations. Our approach addresses both issues.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Probabilistic & statistical models in industrial plant control
Abstract: The paper presents an approach to the development of an intelligent system for real-time prediction of blast-furnace process states. The forecast is based on the analysis of image video sequence obtained as a result of stream video cameras installed on the tuyeres of a blast furnace. Algorithms for recognition of video images of tuyere hearths, as well as scenario forecasting of the evolution of technological situations, are proposed, The research aimes at the analysis of blast-furnace production situations and the prediction of their evolution in real time. This enables timely decisions on adjusting the control in automatic or automated mode. Based on the revealed patterns in the change of video data and by means of the algorithm developed by the authors for process dynamics analysis and prediction, a method is proposed, for early detection of abnormal situations at tuyeres, including the ones enatailing blast furnace destabilization process. Not only the process state at the next time instant is predicted, but also the most probable chain of several subsequent states. For the associative pattern search, Markov chains, machine learning and wavelet analysis methods are used.

Keywords: Smart manufacturing systems, Probabilistic & statistical models in industrial plant control
Abstract: Starting from a critical problem in oil refineries, namely on-specs LPG production, we propose a generic mathematical programming approach that incorporates flow and blending constraints for process industries in which impurities must adhere to certain specifications. Moreover, we extend our approach to accommodate the uncertainty that may arise from the level of impurities in the input feed.

Keywords: Inventory control, production planning and scheduling, Operations Research, Transportation Systems
Abstract: This paper studies a multi-period closed-loop inventory routing problem for perishable food that are carried by returnable transport items (RTIs) of different types. We formulate the problem as an integer linear programming (ILP) model considering RTIs with different food quality preserve ability, and the simultaneous delivery of food and pick-up of RTIs. The objective is to maximize the total profit of the holistic supply chain that equals to the selling revenue minus the summation of production, routing, inventory and RTI purchase costs. The proposed model is demonstrated to be correct and effective by conducting computational experiments on randomly generated instances.

Keywords: Supply chains and networks, Design and reconfiguration of manufacturing systems, Heuristic and Metaheuristics
Abstract: The growth of disruption risks, that is, risks with very low probability of occurrence and very high adverse impacts (e.g., pandemics, earthquakes, etc.), across the global supply chains, has increased over the past decades. On the other hand, the increasing applications of Artificial Intelligence (AI) throughout supply chain practices has led to the emergence of faster and more reliable decision-making methods when large volumes of data challenge the traditional methods. While applying machine learning (ML) techniques is well-documented in the supply chain risk literature, few studies focus on the interpretability of the outcomes achieved by ML. The present study aims to take a step towards fulfilling this gap by using machine learning algorithms on real-world data from an automotive supply chain. In so doing, the performance data of 10 suppliers over two consecutive years were used. A clustering algorithm was first used to generate the labels based on the concept of resilience capacities. Then, since the interpretability of the results were a priority, two interpretable ML algorithms, Naïve Bayes and decision tree, were chosen to classify the suppliers based on their performance with respect to each capacity. The results showed that for interpretable algorithms, decision tree could be potentially a better performing algorithm, yet Naïve Bayes could provide more flexibility and insights through nomograms.

Keywords: Supply chains and networks, Modelling Supply Chain Dynamics
Abstract: The growing competition and the destabilising effects of climate, disease and other external perils bring important challenges to companies worldwide. Supply chain (SC) networks become more and more complex with a growing exposure to a broad range of uncertainties, some of which may cause network disruptions. Neglecting these kinds of risks may lead to adverse consequences, such as negative financial effects, higher transportation costs, order delays, inventory shortages, and loss of market shares. Frequent disruption events that have been continuously increasing over recent years have clearly shown the key role of supply chain resilience. To hedge against SC disruptions, a well-designed and reliable supply network that performs efficiently in normal situations and resiliently during unstable conditions is a top priority. In this paper, starting from the discussion of the main design drivers for a resilient SC, a methodological framework is proposed to support both efficient and resilient supply network design. The procedure gets foundation from the recent literature, and it is directly derived from its application in a numerical example and an industrial case.

Keywords: Modelling Supply Chain Dynamics, Production Control, Control Systems, Inventory control, production planning and scheduling
Abstract: The pandemic created a massive disruption and exposed the lack of resilience in supply chains (SCs), bringing resilience to the spotlight. SC practices have become lean to achieve maximum profitability, with very little or no buffer stocks. On the other hand, with disruptions being the new norm in an ever-increasing uncertain and volatile business environment, organizations' sustainability is being severely tested. A comparison of Legacy and Lean SC practices is carried out in this paper. Legacy practices are costly, inefficient and forgotten but valuable in the context. Lean practices are highly profitable and efficient, but they are insufficient for a firm's survival in a disruption. This paper through the basics of control engineering applications and proposes a methodology to achieve an appropriate dynamic mix of Lean and Legacy SC practices through coordinated demand-driven production to handle disruptions. The work presented in this paper will enable SC managers and practitioners struggling to find solutions to deal with disruptions and help strengthen SCs, albeit at the cost of sacrificing some profitability and efficiency.

Keywords: Supply Chain Management, Modelling Supply Chain Dynamics, Risk Management
Abstract: The goal of achieving resilience has become a top priority as companies respond to the current supply chain crisis and develop strategies to manage future disruptions. This paper will discuss supply chain strategies that are required to promote multiple objectives that include agility, resilience, sustainment and ESG. We review recent empirical research that analyzes the global supply chain strategies that companies have implemented to support resilience. This will include discussion of obstacles for implementing such strategies and an empirically based classification that defines strategy archetypes, based on the linkage of such strategies to operational features. We then consider how emerging analytics tools can support the required analysis in order to balance tradeoffs and risks. The paper concludes with a illustration of how such analytics tools, based on the application of machine learning to big data, can support a data driven exploration of policy alternatives in the context of possible, future scenarios.

Keywords: Industry 4.0, Complex adaptive systems and emergent synthesis in manufacturing, Supply Chain Management
Abstract: The management of the Integrated Healthcare Operating Systems (IHOSs) requires more and more to improve efficiency and resilience. We conceptualize the IHOS as a supply chain made up of operating units interacting among each other to accomplish a common goal (the care), resulting from the adoption of Integrated Care Pathways (ICPs). Then, we rely on supply chain management literature to develop theoretical propositions regarding the influence of complexity dimensions on the resilience and efficiency of IHOS. We also advance the existence of a direct and moderating role of Big Data Analytics on these performance and relationships.

Keywords: Supply chains and networks, Supply Chain Management, Operations Research
Abstract: Today need for replacing linear-oriented production systems with circular-oriented ones is urgent. Circular Economy production networks promote the continuous reuse of resources and products, recapturing value from by-products and end-of-life resources, and minimising resource leakage out of the systems. However, the design and management of CE production networks, although representing an important issue worldwide, has been scarcely investigated so far. In this study, we argue that CE networks should be resilient to better face with frequent and unpredictable disruptions and that structural characteristics may influence it. Through a simulation model, we investigate how the node degree connectivity, by affecting the formation of local and global loop structures, influences the network resilience to different types of disrupting events. Results of simulation suggest that in absence of disruptions, a random -like network is characterized by the highest number of long and short cycles. Instead, in presence of disruptions, our results suggest that short cycles are more robust when node degree connectivity is uniformly distributed as occurring in random and small-world -like structures. On the contrary, long cycles result more robust when node degree connectivity is power-law shaped as occurring on scale-free -like structures. Theoretical and managerial implications are discussed.

Keywords: Supply Chain Management, Risk Management
Abstract: Nowadays, industrial firms are increasingly required to develop resilient supply chains to better face turbulent environments by adapting to unforeseen and frequent disruptions. In this regard, researchers strongly agree that fostering innovation toward circular business models can influence resilience capability development. Findings, however, are still fragmented and sparse. To this aim, a systematic literature review of previous studies is conducted. The results of content analyses are presented, and their implications discussed.

Keywords: Scheduling, Operations Research, Transportation Systems
Abstract: We consider the instance space metric method to the two-station single-track railway scheduling problem. This method has been effectively applied to several classical NP-hard scheduling problems, but was not tested on some actual railway scheduling models. It allows to construct the solutions with guaranteed accuracy in polynomial time if there are some polynomially solvable instance subclasses for the original NP-hard problem. Considering the metrics for the problem parameters space, we develop an innovative approach to solve the particular problem in polynomial time with guaranteed accuracy and make some numerical tests.

Keywords: Scheduling, Operations Research, Optimisation Methods and Simulation Tools
Abstract: This research extends the interpolation approach to approximating the objective function value for the minimization maximum lateness problem. The interpolation approach is defined using a special objective function Lmax(α), which is proven to be continuous and depends only on α transform coefficient. Such a function is proven to be monotonically increasing, and this property is used in the presented modified interpolation polynomial algorithm which is used to estimate the approximation error.

Keywords: Decision Support System, Modeling, simulation, control and monitoring of manufacturing processes, Production planning and scheduling
Abstract: This paper targets to show a practical realization of such contemporary notions of modern industrial IT as ‘digitalization’ and ‘big data’ in referent example of energy producing enterprise. Huge historical databases of technological parameters accumulated by modern industrial control systems has transformed to a precious asset of modern companies. Deep knowledge extraction approach applied to the accumulated data can bring additional benefits. The research grounds on classical methodology of mathematical simulation. Using a programmatic realization of numerical Monte-Carlo procedure we create empirical simulation models of real energy producing assets based on their observed behavior. As a result we are able to conduct so called ‘what-if’ analysis and compare different production scenarios for modeled assets. The investigation approach includes the following steps: approximate the output power characteristics of the power generating units; calculate cross-correlation for all output powers with all available measurements collected by power station SCADA system; create several day working scenarios for research; for each possible approximation and all combinations of influencing parameters conduct Monte-Carlo recalculation sessions to define the scenario that gives better day margin for power plant. Copyright © 2022 IFAC

Keywords: Scheduling, Operations Research, Transportation Systems
Abstract: The paper is concerned with scheduling traffic on a single track between two stations which generate requests for transportation with different release times. These requests are served by a fleet of identical vehicles, each of which can serve (carry) several requests for transportation simultaneously. The single track, used by the vehicles, does not permit traffic in both directions simultaneously. The presented polynomial-time algorithms are based on dynamic programming.

Keywords: Scheduling, Optimization and Control, Operations Research
Abstract: We consider the feasibility to find approximate non-preemptive schedules by metric approach for NP-complete problem of scheduling on two parallel identical machines with precedence delays for jobs or jobs of lengths 1 and 2 with makespan minimization. The execution of the job can be started only after the completion of any of its predecessors. We researched the application of methods looking for optimal solution for P2 | prec, p_j = 1 | C_max. We have tested Coffman's and Sethi's algorithms.

Keywords: Supply chains and networks, Supply Chain Management, Optimisation Methods and Simulation Tools
Abstract: During an epidemic or a pandemic emergency, various approaches are undertaken to contain the infectious disease spread. Some of the most common interventions are lockdowns, social distancing, contact tracing and the use of personal protective equipment. However, whenever available, the most helpful intervention is the administration of vaccinations. Countermeasures need to be taken as quickly as possible in emergencies, but predicting their full consequences and effects is often difficult, mainly because there is no room for trial-and-error approaches. Simulation – in its different implementations – represents a useful approach for modelling and analysing reality and predicting the evolution of a real-world system. Agent-based models could be particularly beneficial as they allow for modelling each individual as a distinct entity, thereby enabling the evaluation of the effects of public policies in the field of interest. This paper reviews the existing literature on agent-based simulation for vaccine distribution and administration. This work highlights areas where agent-based simulation has been most utilised and areas that could be explored further. Specifically, the most significant gaps are the lack of application of agent-based simulations to vaccine distribution networks and the lack of consideration given to resources requirements and costs associated with alternative vaccine administration methods to citizenship.

Keywords: Simulation technologies, Modeling, simulation, control and monitoring of manufacturing processes, Robotics in manufacturing
Abstract: The COVID-19 pandemic has significantly impacted many aspects of our social and professional life. To this end, Higher Education institutions reacted rather vastly to this unpreceded situation although many issues have been reported in the international literature since the emergence of the first global lockdown. As we are now transitioning back to the ‘normality’, universities and businesses consider the so-called ‘blended’ or ‘hybrid’ model as a means of facilitating the transition phase. In view of this decision, several studies can be identified wherein blended learning scenarios are proposed and described. The present work constitutes such an effort. Precisely, while adjusting the lens to the didactic of Robotics courses, we propose a blended learning model via which the laboratory activities are performed without the physical presence of the students in the physical context. The aforementioned objective is attained under the aid of the Virtual Reality technology coupled with the Digital Twin model. We hope that the ideas presented in this manuscript will motivate and inspire more researchers, instructional designers, and educators to consider the adoption of such alternative instructional techniques to mitigate the shortcomings that the remote education setting brings and further to improve the overall learning experience.

Keywords: Supply chains and networks, Decision Support System, Fuzzy logic control
Abstract: The advent of the Covid-19 pandemic has posed severe challenges to health care networks in various countries. The overcrowding of hospitals and the lack of medical staff and beds in multiple wards are among the main problems of governments. A new virus wave also exacerbates these problems. Also, the lack of information and the variability of the incidence rate and severity of the disease in different waves make it difficult to estimate the number of patients accurately. In this respect, this study develops a mixed-integer linear programming model to reorganize the medical service network. A fuzzy approach is employed to estimate the number of patients in each period. The result obtained from the model, apart from preventing the shortage of hospital beds, demonstrates a 60% reduction in visits to these centers.

Keywords: Scheduling, Heuristic and Metaheuristics, Operations Research
Abstract: Due to the COVID-19 pandemic, laboratories have faced unprecedented demand for in-home delivery test services. This drastic demand increase requires a rapid reaction from laboratories to manage their testers in order to respond to the high demand volume and avoid unnecessary costs. This study provides an optimization model based on the vehicle routing problem with time windows by considering the testers' workload balancing to improve laboratories' assignment and routing policies. A medical lab that has faced this situation for its in-home test services is taken as a real-world case in the current study. A mixed-integer programming model is solved for small instances using the CPLEX solver, and an adaptive large neighborhood search algorithm is implemented for large instances. Ultimately, the obtained solutions are compared to the real-world implementation of the lab on a dataset of six consecutive days, and the results are further discussed.

Keywords: Quality management, Pricing and outsourcing, Supply chains and networks
Abstract: Reverse logistics activities can be implemented as an agile solution in response to the shortage in production capacity of certain products during the pandemic and a convenient alternative to the raw materials extraction in post pandemic course. However, collecting and inspecting returned products for different reverse logistics activities is a complex process due to the different conditions in which the products has been kept. We designed a reverse supply chain network with competing collection centers, 3rd party remanufacturers, and recyclers to define the minimum quality of return products based on customer demands in different situations. The equilibrium condition is governed using a variational inequality model. We applied the modified projection method to solve the model.

Keywords: Scheduling, Industry 4.0, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Manufacturers are increasingly under pressure to develop dynamic production systems and supply networks that can adjust to the climate, political, and social changes anywhere in the world at any time. Adoption of the Industry 4.0 paradigm aids in the completion of these objectives. Modern production systems necessitate a high level of manufacturing flexibility. At the same time, to keep up with the competition, manufacturers must make pledges to meet specified deadlines. In recent years, there has been a rise in interest in employing machine learning, particularly reinforcement learning, to solve production scheduling challenges of varying complexity. The general technique is to decompose the scheduling problem into a Markov Decision Process (MDP), after which an RL agent is trained using a simulation that implements the MDP. In this setting, this paper presents, in an application environment, a dispatching rule based on a deep reinforcement learning (DRL) algorithm. A DRL approach uses the DQN as the learning agent's training algorithm. The network's task is to identify the position of the job that will be executed. The objective is to present an algorithm that takes both the due date and the state of the production line into consideration to schedule jobs to meet the due dates and, at the same time, boost productivity. A flow shop configuration is considered and the performances of the proposed method are compared with the ones of dispatching rules already proposed in the scientific literature. To do so, the settings of the DRL algorithm must be specified, such as the state space, the reward function, and the hyperparameters, whereas the action is the choice of which job to be introduced in the production line. The overall objective of this research is to provide a general scheduling tool that may be used in a variety of situations, including unexpected ones.

Keywords: Knowledge management in production, Quality management
Abstract: he diffusion of the Industry 4.0 paradigm has led to a proliferation of data that is generated by production assets on the shop floor. This data opens up new opportunities for the analysis of quality issues, but it also makes identifying, selecting, and correctly interpreting data all the more critical. This involves a multitude of domain experts that design, operate and maintain production equipment. Major challenges they face in this context are (i) to map and integrate their domain knowledge on potential failure modes and effects, products, processes and production assets and (ii) to coordinate their actions to systematically investigate and address the most important issues first. To address these challenges, this paper introduces the FMEA-linked-to-PPR Asset Issue Analysis (FPI) Model, a multi-view coordination asset, to guide quality issue analyses. The model integrates cross-domain knowledge and makes it possible to track the investigation state of quality analyses in teams of domain experts. A preliminary evaluation on a real-world use case conducted with a domain expert indicates the FPI model to facilitate effective cross-domain analytic processes and the efficient identification of potential causes for quality issues.

Keywords: Industry 4.0, Smart manufacturing systems
Abstract: Advances in information technology and sensors, efficient connectivity, and ground-breaking computational ability are proving to be game-changer for improving maintenance strategies and production outcomes. The integration of conventional maintenance concepts with disruptive technological developments leads to significant changes in current maintenance practices in a response to the digitalized context requirements. However, despite the availability of technology and its relative affordability, many maintenance digital transformation (MDT) initiatives fail to accomplish their goals. This is due to the fact that technology alone is not sufficient for an organization to successfully digitalize its operations. Several challenges including the absence of a business case, limited analytical capabilities, uncertainties about new technologies, and impact of the human factor are hindering the success of attempted MDT efforts. MDT enablers reflect various aspects and span different implicated areas of the organization. This study seeks to develop a comprehensive and structured list of the underpinning factors driving the success of MDT. To that end, the authors conducted a systematic literature review in order to extract the various factors highlighted in the literature. Subsequently, a purification phase was conducted which consisted of merging the redundant factors together, then mapping them into various thematic categories based on the aspect covered. The final consolidated list comprises 63 enablers of MDT that are classified into 14 categories. This research enriches the extant relevant literature and provides guidance to practitioners in this field.

Keywords: Knowledge management in production, Smart manufacturing systems, Industry 4.0
Abstract: A trend from centralized to decentralized production is emerging in the manufacturing domain leading to new and innovative approaches for long-established production methods. A technology supporting this trend is Cloud Manufacturing, which adapts technologies and concepts known from cloud computing to the manufacturing domain. A core aspect of Cloud Manufacturing is representing knowledge about manufacturing, e.g., machine capabilities, in a suitable form. This knowledge representation should be flexible and adaptable so that it fits across various manufacturing domains, but, at the same time, should also be specific and exhaustive. We identify three core capabilities that such a platform has to support, i.e., the product, the process and the production. We propose representing this knowledge in semantically specified knowledge graphs, essentially creating three through features interconnected ontologies each representing a facet of manufacturing. Finally, we present an exemplary implementation of a Cloud Manufacturing platform using this representation and its advantages.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control
Abstract: This paper tackles the control design of a robot to handle said interaction. A new model is written of an EMPS Prototype to emulate a robot-environment scenario, including now two masses and with their interaction translated as a spring and damper system, followed by the implementation of a cascaded loop of force-velocity control of the robot axis. A new formulation of the force control is also designed and implemented considering the impedance control theory. Finally, this model and its cascaded loop control is validated against real values through the experiment proving its accuracy.

Keywords: Optimization and Control, Smart transportation, Production Control, Control Systems
Abstract: In this paper, an optimal finite discrete-time linear quadratic tracking (LQT) control method is proposed to control the altitude and attitude of a quadrotor. First, the dynamic model of the quadrotor is derived using Newton-Euler equations. Next, non-linear equations of the quadrotor are written in the state space form and linearized around an equilibrium point. Then, continuous-time linear state-space equations are converted into discrete-time equations considering a specific sampling time. Moreover, the controller design process is completed by determining the performance index and the weighting matrices, and the optimal control input is acquired for the closed-loop system. In the end, the simulation results are shown to demonstrate the robustness of the controller against parameter uncertainties and show its performance in attenuating the external disturbance effect. Copyright © 2022 IFAC (International Federation of Automatic Control). Hosting of Elsevier Ltd. All rights reserved.

Keywords: Robotics in manufacturing, Optimization and Control, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Spinal cord injured patients cannot move their segments by their intact muscles. A suitable controller can be used to help them move their arm. In this study, the kinematics and dynamics of right-hand movement are modeled considering planar three links. A genetic algorithm-based sliding mode (GASM) controller is designed to move the human arm model for tracking a desired trajectory in the sagittal plane. The GA is used to tune the convergence rate of the sliding mode controller for having an appropriate tracking performance. The summation of errors is considered as a cost function and GA is proposed to find the controller gains to minimize the difference between the outputs of the model and nominal trajectories. To the best of the author's knowledge, it is for the first time that the GA-sliding mode controller has been used for controlling the human hand so as to have a particular movement. Simulation results are evaluated in upward and downward movements of the human arm to affirm the effectiveness of the proposed controller.

Keywords: Robotics in manufacturing, Distributed systems and multi-agents technologies, Optimization and Control
Abstract: In this paper, a centralised robust discrete-time sliding mode controller is proposed for the formation control of a multi-quadrotor system in the presence of disturbances. The control system, based on consensus control, is designed around the full, nonlinear, under actuated dynamics of the quadrotor. Graph theory is used to define the communication topology of the multi-agent system. Results obtained through simulation in MATLAB and Simulink are used to verify the control system.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control, Robotics in manufacturing
Abstract: The paper is devoted to a human operator control for an electromechanical remote manipulator. It is assumed that a human operator in a closed-loop achieves the goal of control by deflecting the joystick. A saturation nonlinearity is taken into account in the paper. To compensate for the negative phase delay in the system performance, a corrective filter is introduced to the actuator loop. For such a nonlinear system the identification algorithm of the parameters of the human operator model is proposed. The algorithm relies on an optimization method with constraints imposed on the control system parameters. The simulation results demonstrate the dependence of human operator model parameters on the controller gain. The nonlinear filter application in a control loop with a saturated actuator demonstrates an improvement of the system performance and the oscillations prevention of the output coordinate.

Keywords: Inventory control, production planning and scheduling, Production Control, Control Systems, Simulation technologies
Abstract: RF-POLCA has been proposed as an alternative to POLCA. This is production control mechanism simpler to implement that uses authorization dates only at the first cell in the POLCA chain. Although POLCA has been extensively studied, the performance of RF-POLCA largely remains unknown. RF-POLCA is here studied and compared with POLCA considering: POLCA chains dimension; processing times variability; and shop configuration. The study is carried out using discrete event simulation. Results for the divergent and convergent shops show that RF-POLCA performs better than POLCA for shorter chains, while performing worser for longer chains. In the flow shop, where there are few intersections between POLCA loops, RF-POLCA performs better than POLCA for both, short and longer chains.

Keywords: Inventory control, production planning and scheduling, Supply Chain Management, Production Control, Control Systems
Abstract: Abstract: In this paper, we are interested in the stock control in the closed-loop supply chain and its application to the cylinder packaged gas supply chains of Air Liquide company. Air Liquide is a multinational company producing and distributing industrial and medical gases. Firstly, we introduce a new classification of demand profile and perform a comprehensive study of the goodness of fit of demands sizes and time between successive demands based on real data. As well known in the literature (Boylan and Syntetos (2021)), we find that negative binomial distribution fits better demand sizes and intervals. Secondly, we explain how to use a single echelon model that is easy to implement on the field, to control inventory and compute the stock target in the observable part of the closed-loop supply chain. It is modeled as a multi-echelon serial inventory system. Finally, we perform numerical analysis and comparisons of methods to compute the stock target of the observable part of the closed-loop supply chain with real data. Those methods are derivated from the up-to-level periodic inventory review policy where we vary the statistical distribution of the demand and the measure of the service level. Using the Item Fill Rate (IFR) as a service level measure in the stock target calculation, reduce the stock target by more than 10% compared to the stock target obtained using the Cycle Service Level (CSL). We also investigate methods using compound Poisson distribution for demand models.

Keywords: Inventory control, production planning and scheduling, Sustainable Manufacturing, Operations Research
Abstract: In this paper, we develop an economic production quantity (EPQ) model with inventory rationing for a decaying item under deterministic conditions. Two demand classes are considered, and inventory rationing is implemented by introducing a critical level: when stock is below the critical level, low-priority demand is rejected. The cost model is developed considering two situations, depending on whether the critical level is in the inventory build-up phase or in the inventory depletion phase. We then formulate the optimization problem and propose a solution procedure. Numerical experiments are finally carried out to demonstrate the benefits of implementing inventory rationing.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Code generation is an effective way to drive the complex system development in model-based systems engineering. Currently, different code generators are developed for different modeling languages to deal with the development of systems with multi-domain. There are a lack of unified code generation approaches for multi-domain heterogeneous models. In addition, existing methods lack the ability to flexibly query and transform complex model structures to the target code, resulting in poor transformation efficiency. To solve the above problems, this paper proposes a unified approach which supports the code generation of heterogeneous models with complex model structure. First, The KARMA language based on GOPPRR-E meta-modeling approach is used for the unified formalism of models built by different modeling languages. Second, the code generation approach based on graph pattern matching is proposed to realize the query and transformation of complex model structures. Then, the syntax for code generation is integrated into KARMA and a compiler for code generation is developed. Finally, a case of unmanned vehicle system is taken to validate the effectiveness of the proposed approach.

Keywords: Design and reconfiguration of manufacturing systems
Abstract: To shorten times-to-markets, also the planners of production systems have to be supported appropriately. For this, open, adaptable systems are needed which support continuous flows of information, e.g. by leveraging standard data formats – and, which are easy to use for planners without specific knowledge about software development or ontology engineering. This paper introduces a support system for production system rough planning, especially but not limited to assembly systems, which consists of two main components: a standard-based ontology as explicitly formulated external data model and a relatively universal software system working on the ontology, thereby focusing mainly on the ontology.

Keywords: Design and reconfiguration of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes, Production planning and scheduling
Abstract: The development of an aircraft industrial system faces the challenge of integrative requirements validation with de-correlated modelling languages and distributed proprietary formats. This paper specifies an ontology-centric industrial requirements validation based on a cognitive digital twin approach, aiming at addressing the potentials of utilizing MBSE ontology integration for different models and leveraging a top level ontology BFO as a semantic core to integrate cross-disciplinary requirement validation, heterogonous models and simulations towards an optimized digital consistency, interoperability and reusability, with an example of such requirements validation presented.

Keywords: Modelling Supply Chain Dynamics, Supply Chain Management, Supply chains and networks
Abstract: Under the premise of stochastic market demand, this paper studies the decision-making problem of dual source procurement from a foreign supplier with low purchase price but poor stability and a domestic supplier with high purchase price but good stability. First, we establish a benchmark system dynamics model composed of manufacturer, foreign suppliers and suppliers, and solve the optimal decisions and profit of manufacturer under different reliability of foreign suppliers. Second, we consider tariff factor in the benchmark model, and two different models are compared. The simulation results reveal the influence of tariff on the whole supply chain, which is helpful for enterprises to conduct domestic and foreign dual source purchasing.

Keywords: Inventory control, production planning and scheduling, Heuristic and Metaheuristics, Sustainable Manufacturing
Abstract: This study proposes a mixed integer linear programming (MILP) model for the single-item multi-period capacitated lot-sizing problem with remanufacturing. The tackled problem is capacitated in terms of production and storage. The production capacity is overcome by allowing for overtime in some periods. Moreover, the customer demand is assumed to be dynamic and deterministic. Demand is satisfied by either producing new items or remanufacturing returned items. The objective is to obtain an optimal plan over a finite planning horizon. The structure of the problem results in strongly NP-hard problem. Consequently, we develop a variable neighborhood search (VNS) metaheuristic algorithm for solving this problem. We demonstrate the robustness of the proposed VNS approach by examining different problem instances with different sizes. The obtained results are compared with the output of CPLEX solver in GAMS. The results obtained from the proposed metaheuristic outperform the solver output in terms of solutions quality and computational time.

Keywords: Supply Chain Management, Operations Research, Heuristic and Metaheuristics
Abstract: In this paper we propose an Approximate Dynamic Programming approach for the integrated fleet sizing and replenishment planning problem utilizing fix and optimize algorithm. The problem is about deciding both the composition of a fleet for distributing a single item and the replenishment planning based on a predetermined delivery frequency. This problem integrates two important logistical issues namely fleet sizing and replenishment planning. The objective is minimizing all relevant costs composed of vehicle ownership, inventory, and approximate routing costs. We show the effectiveness of the proposed solution methods on a set of fairly large size randomly generated problems.

Keywords: Inventory control, production planning and scheduling, Facility planning and materials handling, Design and reconfiguration of manufacturing systems
Abstract: The ongoing pandemic, namely COVID-19, has rendered widespread economic disorder. The deficiencies have delayed production at manufactories in several industries on the supply side. The effects of disruption were more notable for industries with longer supply chains, especially reaching East Asia. Regarding the demand, sectors can be divided into three categories: i) the ones, like e-commerce companies, that experienced augmented demand; ii) the ones with a plunged demand, like what hotels and restaurants experience; iii) the companies experiencing a roller-coaster-ride business. After mitigation efforts, the economy started recovering, resulting in increased demand. However, regardless of their struggles, the companies have not fully returned to their pre-pandemic levels. One of the strategies to gain resilience in its supply chain and manage the disruptions is to employ flexible/hybrid manufacturing systems. This paper considers a flexible/hybrid manufacturing production setting with typically dedicated machinery to satisfy regular demand and a flexible manufacturing system (FMS) to handle surge demand. We model the uncertainty in demand using a scenario based-tree approach and allow the business to make here-and-now and wait-and-see decisions exploiting the cost-effectiveness of the standard production and responsiveness of the FMS. We propose a column generation-based algorithm as the solution approach. Our computational analysis shows that this hybrid production setting provides a highly robust response to the uncertainty in demand, even with high fluctuations.

Keywords: Supply Chain Management, Inventory control, production planning and scheduling, Operations Research
Abstract: Recently, online dominant retailers have provided installment sales (IS) to their con- sumers to alleviate their financing pressure. Meanwhile, the upstream suppliers need financing to fulfill the procurement from their retailers. In this paper, we consider an online dominant retailer as an intermediation, that helps her suppliers get financing from banks, and provides installment payments to consumers simultaneously. In particular, we describe customer’s willingness to buy through both consumer’s preserved value and income level. By constructing game-theoretical models, we discuss the equilibrium strategies of both commercial loan only scheme(CL) and commercial loan with installment payment scheme. Under information asymmetry between supplier and bank, we find that the interest rate of the supplier’s loan decision under CL will be higher than the risk-free interest rate. The retailer under CL will offer a higher wholesale price with IS than without IS due to larger sales under IS. Numerical experiment shows installment payments mode can improve supply chain performance by increasing both supplier’s and retailer’s profit. And the retailer’s profit distribution growth from IS is far greater than that of the supplier.

Keywords: Supply Chain Management, Inventory control, production planning and scheduling
Abstract: Different from bank credit rating, the dominant supplier usually ranks retailers to different credit ratings according to their transaction history, such as their order quantities. In this paper, the retailer is ranked either at low credit level or high credit level according to the retailer's historical order quantities. The retailer with low credit rating can only operates from hand to mouth whereas the retailer with high credit rating can apply for partial trade credit as well as a bank loan through the credit guarantee provided by supplier. We analytically derive the retailer’s optimal ordering and financing policies. The trade credit and bank financing model is shown to be better off than self-financing model in terms of the retailer’s optimal ordering. Hence it is worth to implement credit rating differentiation by the supplier.Observing from the retailer's ordering behavior, the upstream supplier can gain more information on its downstream’s capital position through differentiating the credit level by ordering.

Keywords: Modelling Supply Chain Dynamics
Abstract: The third party payment service provider and bank are the two main participants of mobile payment markets. In this paper, duopoly game models between the third-party payment service provider and bank with complete information are constructed and investigated. The conditions for the existence, stability, and bifurcation of equilibria in duopoly Nash game and multi-period game are obtained. The effects of adjustment parameters on business volume and profit are discussed. Direct ooperation is introduced to meet the rapid expansion of mobile payment market. It is found that direct cooperation can increase business volume and suppress the emergence of chaos in mobile payment markets. Numerical simulations are llustrated which agree well with our theoretical analysis.

Keywords: Transportation Systems, Monitoring, diagnosis and maintenance of manufacturing systems, Operations Research
Abstract: Anomaly detection is a topic studied in the literature for a large number of applications. As more and more data is collected continuously, numerous algorithms have been developed for anomaly detection in time series. In this paper, we first propose a method for anomaly detection in time series that can be used in different fields. The method is then applied in a real case study for the detection of anomalous behavior of hybrid trucks. Finally, the results obtained are analyzed to propose lines of improvement. Keywords: Anomaly detection, time series, method, vehicle monitoring, hybrid trucks.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Sustainable Manufacturing, Industry 4.0
Abstract: The management of the geographically distributed assets of irrigation and drainage water grids compels a challenging alignment of operations like maintenance plans, resource allocation, and spare parts inventory management systems. We propose an IoT-based maintenance framework to optimize reliability, management costs, and logistics and increase the resilience and sustainability of the irrigation and water management network. The proposed framework provides an interface between the centralized inventory system and the maintenance and logistics operations carried out throughout the network. Information exchange is supported by bar code and RFID technologies integrated within the IT system of the company. A Maintenance Management Software (MMS) able to store assets' physical hierarchy data and inventory data is the supporting tool that fuels the framework. The framework is applied to a regional drainage water system in the Emilia-Romagna region (Italy), improving the visibility of the infrastructural network and enabling tailored and data-driven preventive maintenance plans. The combination of the components' availability, logistics and maintenance costs, failure trend, and impact on the overall network contribute to establishing new methods for assessing the threshold of service level (i.e., reliability) to guarantee for each material.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: This paper reports on an empirical study undertaken to investigate the overall equipment effectiveness (OEE) of several strategically selected production lines in a manufacturing plant. The study involved the development and application of bespoke data acquisition systems to capture shop-floor level data, as well as the validation of a novel OEE model that accounts for a set of industry-specific requirements and conditions. Based on the analysis of data acquired from a number of production lines within the plant over several months of operation, an OEE model that computes the ratio of the target time, with no disruptions during production, to the actual time, was proposed. Novel methods for discriminating delays and delay causes from production time were also validated. The significant time losses identified were related to changeover, order change, and raw material loading. The measure proposed can be applied at the level: including organisation, production line, product, or order. A number of measures for improving OEE are proposed.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Production Control, Control Systems, Probabilistic & statistical models in industrial plant control
Abstract: This work is focused on the issue of semiconductor anomaly detection during the manufacturing process. It proposes an efficient multivariate time-series fault detection approach aiming to detect wafer anomalies at an early fabrication stage to reduce the yield loss. The raw data consist on eleven (11) multivariate time-series (MTS) measured for 150 seconds and collected during different levels of the fabrication process to describe the wafers status. First of all, the most useful information is extracted from each collected time-series (TS) data to handle the computational complexity of large-scale data processing. For that, three dimensionality reduction techniques, namely: (i) Independent Component Analysis (ICA), (ii) Principal Component Analysis (PCA), and (iii) Factor Analysis (FA) are used for comparison and optimization sake. The aim is to define the better technique allowing to keep only the meaningful information from each time-series. Thereafter, the extracted data is combined to build a new dataset which is used to fit and optimize a multilayer perceptron (MLP) to perform the anomaly detection. The very interesting obtained results show that the proposed approach is promising and could provide a precious decision-making support for abnormal wafer detection in the semiconductor manufacturing process.

Keywords: Supply chains and networks, Supply Chain Management, Risk Management
Abstract: The increasing risk of natural disasters has a direct impact on the survival of the affected people and further seriously jeopardizes the sustainability of the country's economic growth and development. The high volatility of disasters and the skyrocketing demand for relief operations pose enormous challenges to the humanitarian supply chain (HSC). At present, most developing countries lack resilient and effective HSCs. Therefore, in this work, by qualitatively tapping into HSC risk factors, a Bayesian Network (BN) model is developed for quantitatively evaluating the performance of HSC in the context of the rising threat of tropical cyclones in Zimbabwe. The BN model would also further help humanitarian relief organizations to gain some management insights.

Keywords: Modelling Supply Chain Dynamics, Risk Management, Optimization and Control
Abstract: Supply chain (SC) has been continuously affected by the epidemic and political situation, which has greatly impacted the SC disruption risk management. This situation evokes attention to control the SC inventory management under ripple effect. In addition, the need for supplier selection and disruption risk management can affect the SC inventory management over the planning time horizon. However, most existing works focus on inventory management, supplier selection, and disruption risk assessment separately. This work investigate an integrated inventory management, supplier selection, and disruption risk assessment under ripple effect. For the problem, a novel mixed-integer optimal control blended with dynamic Bayesian network (DBN) is formulated. The mixed-integer optimal control, proposed by Sager (2005), can portray the dynamic process consisting of continuous and discrete control variables. The DBN is applied to establish probabilistic relationships among SC participants, and to evaluate the disruption risk of the manufacturer. Two performance indexes are proposed to minimize the total cost and the disrupted probability for the manufacturer simultaneously. Then, a solution method is established to solve the problem, and an illustrative example is presented to demonstrate the proposed method.

Keywords: Facility planning and materials handling, Robustness analysis, Operations Research
Abstract: This paper examines the prepositioning of emergency supplies problem, which integrates the decisions of facility location, emergency supplies prepositioning, and distribution under predictable disasters. We scrutinize three stages of relief management in the context of predictable disasters. After that, this paper introduces a novel three-stage distributionally robust optimization (3DRO) model. To make the 3DRO model computationally tractable, we further develop a deterministic equivalent model. A real case study in China demonstrates the superiority of our proposed model.

Keywords: Supply chains and networks, Operations Research, Transportation Systems
Abstract: This paper deals with the problem of optimizing the logistics network of a Third-Party Logistics (3PL) company. The goal is to minimize the cost of storage and transport operations, in the case of multiple warehouses, multiple suppliers, multiple customers, multiple products and multiple types of transportation vehicles. A new Mixed Integer Linear Program (MILP) model is proposed for this interesting problem. The related decisions include selection of (a) the warehouse(s) to store each product, (b) the inventory level per product per warehouse, (c) the warehouse(s) to serve each customer and (d) the appropriate vehicles to transport the products from the suppliers to the warehouses, and from the latter to the final customers. The model was applied in case study of a 3PL company in Greece to optimize part of its supply chain that comprises three warehouses, 23 suppliers and 53 customers. The results obtained were very encouraging, since overall warehousing and distribution costs were lowered by 10.84% compared to the way the company operates currently.

Keywords: Industry 4.0, Sustainable Manufacturing, Operations Research
Abstract: Knowledge about the three paradigms namely Industry 4.0 “I4.0”, Lean six Sigma “LSS” and Circular Economy “CE” has been largely explored separately or in dual combination. However, the connection of the three concepts is not yet well explored in the literature and needs to be developed. The main purpose of this paper is to review the research trends on the connection between the three based on content analysis. Studies published on the Scopus database from 2017 to 2021 were explored resulting in 76 papers. Six themes were identified. Most papers discuss the combination of “LSS and I4.0 "and “The perspective of CE in industry 4.0 context” to improve operations management and achieve sustainable performance. We proposed an integrated framework based on the findings of the literature for the integration of circular, smart, lean, and six sigma in the context of manufacturing. This study is novel because it fills an important gap that is lack an integrated framework and provides insights into the relationship between LSS, I4.0, and CE. Our results remain to be discussed and enhanced in future studies

Keywords: Supply chains and networks, Modelling Supply Chain Dynamics, Supply Chain Management
Abstract: Industrial symbiosis has been recognized as a promising strategy to move towards a Circular Economy. However, its impact on the supply chain dynamics is still unexplored. For this reason, with this work we aim to contribute to this research gap analysing the dynamic performance of symbiotic supply chains. Through the agent-based modeling technique, we study two identical three-echelon supply chains, where there is a symbiotic exchange of waste between two manufacturers. We analyse a set of scenarios based on different demand/supply trade-offs and lead time of the treatment process for the waste, and evaluate them in terms of bullwhip effect. Based on our results, we state that the volume of order decreases with the increase of the symbiotic flow, while the order variability increases with it. Our findings suggest that, under certain conditions, symbiotic supply chains can turn into self-contained systems.

Keywords: Decision Support System, Supply chains and networks, Supply Chain Management
Abstract: The COVID-19 vaccination process in Colombia has been a major challenge not only in terms of public health but also in terms of supply chain management and logistics processes. To support the monitoring of these processes and associated decision-making, a dashboard was designed in Google Data Studio focused on analyzing the progress of COVID-19 vaccination and its logistics efficiency. This article describes the design and implementation of the dashboard using a design science approach and discusses the main lessons learned. During its development, four major challenges were identified: the search for and availability of data sources, the definition and standardization of metrics, the extraction of data in different formats; and finally, the validation of the metrics. Despite these challenges, the dashboard became a source of information for different stakeholders in the Colombian COVID-19 vaccination network, facilitating the monitoring of key performance indicators (KPIs), supporting decision-making, and policy evaluation. This reaffirms the importance of having open information to generate knowledge for both public and private entities as well as for the public. The main contribution of this work is the definition and standardization of the KPIs and it is therefore expected that this experience will serve as an insightful input for designing mass vaccination strategies.

Keywords: Operations Research, Scheduling, Optimisation Methods and Simulation Tools
Abstract: Over the years, Specialized Education and Home Care Services (SEHCS) have found their place in the French social in order to support people with visual, hearing or physical disabilities and the overall aging of the population. The development of these activities has brought to light new issues within SEHCS establishments. In this paper, we proposed an mixed-integer linear programming mathematical model to solve the assignment, scheduling and routing problems of a SEHCS. The model is validated through small and middle size instances inspired by a real-life SEHCS example and the solver Gurobi is applied to find the optimal solution. The experimental results highlight the efficiency of our approach since the optimal results show that the solutions perform well as the SEHCS assignment, scheduling and routing problem can be solved in a reasonable time with optimal solution.

Keywords: Operations Research
Abstract: In commercial transactions, the main goal of a company is to minimize costs or maximize profits. In the last years, the importance of optimizing alternative goals has become more and more critical, especially in those domains where business activities are focused on providing services to products (by integrating them into the product itself - servitization), or more importantly, to persons (healthcare systems). In these contexts, the satisfaction of customers (patients) is as relevant as that of employees (professional care givers) to guarantee an effective and efficient level of service. The introduction of the concept of fairness allows to separately consider the optimization of employee-centered and customer-centered measures, possibly working out positive interactions among the two categories. In this paper, we analyze several measures of fairness both patient-centered and nurse-centered within a vehicle routing problem where nurses are routed to visit patients at their homes. We provide the mathematical formulations for different fairness measures and compare their results on a set of benchmark instances drawing some interesting conclusions on their interactions and possible impact in terms of costs.

Keywords: Smart transportation, Supply chains and networks, Supply Chain Management
Abstract: The last-mile delivery in urban areas is the biggest challenge for carriers. In recent years, e-commerce has experienced strong growth, which has caused an explosion in the number of packages to be delivered. Businesses no longer hesitate to invest in new technologies such as artificial intelligence and drones. In this paper, we present a model of autonomous truck delivery resupply by drones, introducing the concept of transshipment point. Through comparisons with other delivery models, we show that our solution can be economically viable and improve the lives of inhabitants and customers.

Keywords: Smart transportation, Industry 4.0, Supply chains and networks
Abstract: This study presents a reverse logistics network design (RLND) model for medical product waste management generated as part of home health care operation. The aim of this paper is to develop a mathematical model that maximizes collected waste amount by utilizing real-time updating number of customers in the network and their changing demands. The efficiency and applicability of the developed model was validated by a randomly generated small dataset based on a case study in home care health management sector using Pyomo optimization solver package

Keywords: Optimization and Control, Transportation Systems, Operations Research
Abstract: Many real-world problems related to the route such as its fees, ecological footprint, duration, and/or distance. We propose a bi-objective formulation for this problem named Green Vehicle Routing Problem with Time Windows Temporal Dependency Multiple Structure and Multiple Specialties “GVRPTW-TD-2MS” in home health care field. It aims in the first degree to optimize the economic and environmental aspects. It determines the shortest path caregivers’ tours made by alternative fuel vehicles with the minimum carbon emission produced and alternative fuel stations shared. The social aspect is considered as the second concern in our research paper which, maximize the patients’ satisfaction levels

Keywords: Smart transportation, Transportation Systems, Decision Support System
Abstract: Buses are expected to be punctual, but deviation from the schedule is a common occurrence. Transit signal priority (TSP) and conditional signal priority (CSP) are methods to help the bus perform the schedule better by giving traffic signal priority to the bus. Markov decision process (MDP) is suitable for modeling such sequential decision process. In this article, we point out the Markov property of the system based on the analysis of the bus driving process. We model the process of bus driving with CSP as a Markov decision process. Then, the Deep Q Network (DQN) algorithm is applied to solve this MDP model. To the best of our knowledge, this is the first time that the bus scheduling problem with CSP has been modeled as an MDP model and solved by a learning algorithm. Numerical experiments verify the applicability of the DQN algorithm to solve this MDP model.

Keywords: Transportation Systems, Heuristic and Metaheuristics, Supply Chain Management
Abstract: Disaster relief operations are characterized by the need for a fast and cost-efficient response to provide the affected population with the basic supplies required for survival. The emergency water distribution problem arises when the disaster affects the water supply system. Thus, distributing water by trucks is a quick solution to respond to the disaster in a humanitarian context. The problem that is studied in this paper aims to optimize the routing decisions for the water distribution trucks that minimize the sum of operational costs plus the population deprivation cost. The deprivation cost is associated with the economic valuation of human suffering by lack of access to water. We propose a mathematical formulation for this problem based on mixed-integer non-linear programming and a metaheuristic algorithm to compute solutions. Experiments are performed inspired on a real setting of a disaster preparation plan for several municipalities in Cundinamarca (Colombia) to test the capability of the algorithm. Our results show that including the deprivation costs in the objective function significantly modifies the search space of the problem when compared to a classical VRP. Further, the study provides insights on the impact of the fleet size on the operations’ expected performance indicators such as the last delivery times. In fact, we determine the number of vehicles that are required to complete the water distribution operation in less than 48-hours.

Keywords: Smart transportation, Transportation Systems, Decision Support System
Abstract: Port terminals are critical nodes in the maritime transport network and play a significant role in the global supply chain. However, they still suffer from many disruptions entailed by their complex environment leading to many challenges. With the maritime digital transformation, ports and ships produce significant amounts of data offering an opportunity to use Machine Learning techniques to address some issues and support port terminal operations management. This paper addresses the problem of vessel arrival times prediction to destination ports using Machine Learning models and vessels' historical trajectories data. This paper also provides a structured overview of research work concerning the contribution of Machine Learning techniques in handling port terminal concerns. The existing literature shows that related work has tackled different problems, but further development is needed.

Keywords: Transportation Systems, Optimisation Methods and Simulation Tools, Decision Support System
Abstract: Solving the forestry logistic problem is involved with multiple short- and long-term operational decisions such as truck routing, inventory management, and scheduling. Although these problems are separately studied in the literature, but in many situations, a near-optimal solution of the "comprehensive" problem suffices the managerial needs and directions. In this paper, we consider the forestry logistic and use a simulation-optimization method to reach a near-optimal solution. This method enables us to incorporate different uncertainties such as stochastic travel time and occurrence of external factors. We use real-world data to evaluate our model and measure the complexity of real-world constraints.

Keywords: Distributed systems and multi-agents technologies, Decision Support System, Sustainable Manufacturing
Abstract: This paper presents a cloud platform dedicated to industrial resource sharing between companies. The interactions between these companies are supervised by a multi-agent system and the management of resources is made with the help of an Internet of Things network and a consortium blockchain platform. In this paper, we only describe the matching process by introducing the agents and their collaboration. The matching problem consists of the allocation of resources offered by a company to several companies on the basis of costs minimization and the adequacy between demand and supply for each actor and the level of service maximization. A numerical example is proposed to highlight our proposal.

Keywords: Supply Chain Management
Abstract: Unexpected events are known to be external sources of disruption for global supply chain. These events generate long-term, deep, and serious negative impacts. In this respect, organizations are developing many capabilities to increase their resilience. In this study, we propose an order for capabilities to develop. To reach this objective, capabilities are prioritized through the Fuzzy Analytic Hierarchy Process (FAHP). First, we identified 15 capabilities related to resilience by conducting a literature review. Second, industrial experts were asked to make comparison between them based on their experience with Covid19 as a pandemic event. Finally, we calculate capabilities global weight and generate the ranking. The results of this study indicate that responsiveness, flexibility, readiness and adaptative capabilities are the most needed for a resilient supply chain. The paper can help decision-makers to increase their supply chain resilience by adopting these capabilities. Future research will analyze practices related to these main capabilities.

Keywords: Supply chains and networks, Supply Chain Management, Optimization and Control
Abstract: Reusable plastic containers (RPCs) prevent packaging waste in the food supply chains. Food Catering Supply Chain (FCSC) made of multi-stage logistic networks represents a challenging scenario for adopting RPCs to optimize, particularly when the container's flow meets the food supplies. This paper fosters the application of RPCs in such FCSC by proposing a food-ordering MILP model to aid the cross-docking player in selecting the suppliers and releasing packaged food orders efficiently. This model optimizes logistic costs and operations as well as the influence of the container pooler's facilities network in the FCSC. A numerical example extracted by a larger case study provides validation of the model and offers insights for future research investigations

Keywords: Design and reconfiguration of manufacturing systems, Smart manufacturing systems, Industrial and applied mathematics for production
Abstract: Today's manufacturing is far from traditional in the inclusion of solutions that make it possible to take advantage of the information generated within the production lines. In the same way, the new generations of professionals have skills and access to information that allow them to exploit their capacities and channel their knowledge. One of the current problems is identified as the excess of available information that is not fully used due to the lack of structure, that is, graduate students have the knowledge and sources of information, but during the development of projects they find themselves with paths that are far from the solution. Therefore, between these two concepts is the design and development of entities making use of reference models that integrate toolboxes, engineering activities and methodologies that provide structure during design and development work. In this work, the S3 process development reference framework is presented, which allows the development of manufacturing processes, it is particularized in one of its applications, the selection of solutions for the redesign of a manufacturing process. For this, the case study of a leak test in the automotive sector has been selected. The results of the study provide a structure for development teams to increase the automation of manufacturing processes and align the sustainable objectives pursued by the manufacturing firm with those implemented in the manufacturing process.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Optimization and Control, Production Control, Control Systems
Abstract: Software plays a key role in modern automated systems. Testing the control software is one of the final steps in the development process and when done on the physical set up, it creates risks (e.g. project delay, system damage and operator injury). In addition, with flexible systems it is almost impossible to test every possible scenario on the real system. Using a digital model allows testing different configurations before the physical hardware is available and thus without risk for the operator and the system hardware. A digital model of the reconfigurable system can also help validate new configurations virtually while the real system still operates in its current configuration, allowing for a quick risk-less changeover. Depending on the level in the control hierarchy, the simulations can run faster than real time. This paper demonstrates the use of a digital model during the commissioning at different levels. On each level, other requirements need to be fulfilled and a different level of detail is needed in the model. Every level asks for a different set of input data, that needs to be consistent to result in a reliable emulation/simulation. The methodology is evaluated on a reconfigurable assembly system, consisting of multiple base units with modular, relocatable add-on modules. The most detailed level is the process and machine level, considering the low-level control logic, movements of the machine and robots, collisions, etc. Cell and plant level commissioning focuses on the interaction of multiple machines, transport, production schedule and assignment of tasks to the individual machines. This level typically covers a longer time span and requires less detail of the actions on the machine level. It interacts with higher level control software and a simplified version of the low-level control logic is implemented in the model itself. This paper describes the methodology and lessons learned to reuse the digital model at different levels of the control hierarchy while remaining the consistency by exchange of information within the digital model and with the real system. Further research is described towards automated virtual test case scenarios with a digital model for a reconfigurable assembly system.

Keywords: Design and reconfiguration of manufacturing systems, Inventory control, production planning and scheduling, Decision Support System
Abstract: To remain competitive in a highly dynamic environment, manufacturing companies have to quickly react to disturbances or changing customer requirements. To enable manufacturing systems to cover these dynamics, the concept of reconfigurable manufacturing systems was introduced. From a technical point of view, this concept has been exploited the past 20 years, revealing several different design solutions. However, industrial application is still an exception. Our analysis lead to the assumption that this is due to a lack of operator support for reconfiguration management. In addition, mostly individual aspects of reconfiguration are considered instead of exploiting the entire reconfiguration space at system and machine level. Therefore, in this paper we present reconfiguration management as a holistic problem. For this purpose we present the problem set up and show an overview of possible goals that can be achieved by reconfiguration management.

Keywords: Design and reconfiguration of manufacturing systems
Abstract: The two main objective functions for designing the manufacturing system are, improving the manufacturing system's productivity and production quality. Process planning is an integral part of manufacturing system design. In this work, the study of process plan evolution over the period has been reviewed, keeping in mind the design objectives. Based on the technological advancements, the process plan evolution has been classified into two periods. The first evolution relates to automation, in which efforts are made to automate the manual activities of conventional process plans, which leads to the development of Computer-Aided Process Planning (CAPP). As long as the systems are deterministic with fixed structures and capabilities, CAPP is a good solution. Due to the uncertainty in the market for product demand, the new manufacturing systems are becoming more and more dynamic to handle the product variety demand. In that case, just automation is not enough to achieve the objective functions of system design. Designers have to consider the integration of the manufacturing system life cycle. So the second evolution of the process plan relates to, the consideration of performance indicators defined due to the system integration. For the second evolution, the literature review of the reconfigurable process plan (RPP) is performed considering both automation and integration of the system.

Keywords: Industry 4.0, Risk Management, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: In the totally unprecedented context of the Covid-19 health crisis, the widespread adoption of Industry 4.0 technologies, and the great interest in resilience, have been stronger than ever. Within this framework, the present paper outlines the involvement of technologies emerging from the fourth industrial revolution in the fight against the epidemic expansion, and the results of this implication in terms of strengthening and achieving resilience in diverse fields. In order to gain a fuller understanding of these points, sixteen resilience domains related to the COVID-19 pandemic are defined. On the other hand, the third section of this paper digs into the literature to expose a variety of Industry 4.0 solutions developed to cope with the sanitary crisis. Afterwards, a fuzzy cognitive map is elaborated, using mental modeler, in order to emphasize the causal links between Industry 4.0 technologies and resilience domains. Subsequently, a simulation of this model is performed to evaluate the contribution of an optimized joint use of industry 4.0 core technologies in the achievement of resilience in its different dimensions during the Covid-19 pandemic, and to discuss how the identified gaps or weaknesses can be addressed.

Keywords: Industry 4.0, Sustainable Manufacturing, Risk Management
Abstract: Industrial performance management can be seen as a decision-making process that aims to ensure the performance, results, and utilization of resources towards the achievement of a set of pre-settled objectives. Essentially, this model assumes that the objectives and allocated resources are related to the cost, quality, and delivery criteria under efficiency, relevance, and effectiveness constraints. However, the scope of this model is currently questioned due to the development of the Industry 4.0 initiative and the emerging risks for the human wellbeing and the society, basically since it could be extended to current needs. Industry 4.0, where digital transformations, smart systems and a profusion of data are given place, would offer scenarios in which the company, human and society sustainability can be threatened. Therefore, including the ethical notion on the use of the technological advances is considered fundamental against potential risk, such as data privacy mismanagement, surveillance policies, discrimination, and automated judgement. As a first step of the inclusion of ethics in the industrial performance management, this paper focuses on the basic elements of the performance model that are the objectives, the means or resources, and the results of the performance management. For this, it introduces a set of requirements that defines the ethical awareness of basic elements. The methodological approach taken on this study is an explorative and interpretative research, constructed collectively by a set of researchers and industrial practitioners, based on previous works on ethics. This set of requirements will serve as a benchmark framework enabling companies to compare their performance from an ethical-aware perspective and launch plan of actions to reach the proposed standards.

Keywords: Risk Management, Optimisation Methods and Simulation Tools
Abstract: To manage risks against unexpected cost overruns, project teams use Contingency Budget (CB). Its accurate estimation has been a subject of multiple studies proposing either deterministic or probabilistic models. In this study, we propose a deterministic Machine Learning-based approach to estimate CB. Based on the k-means clustering, our model integrates the Expected Monetary Value (EMV) method and binomial distribution concepts. We test our methodology using 20 risk registers containing 25 risks with associated probabilities and impacts. Using Monte Carlo simulation, we compare our model’s estimates with the ones by the traditional EMV. The model provided more accurate CB estimates and is more straightforward in use than the Monte Carlo simulation.

Keywords: Design and reconfiguration of manufacturing systems, Industry 4.0, Risk Management
Abstract: Given the fourth industrial revolution (i.e., Industry 4.0), a cloud manufacturing (CMfg) system is introduced as a modern service- and customer-centered manufacturing paradigm. This system incorporates the distributed manufacturing corporations to collaborate as an interconnected system and share their production capabilities or resources without any stoppages. In this regard, this paper develops a novel two-stage bi-objective mathematical model for designing resilient CMfg configuration by maximizing the platform’s profit and maximizing the resilience level of the designed CMfg network. Three primary resilience indicators, including design quality, proactive capability, and reactive capability, are considered in the proposed model. Moreover, to evaluate the resilience level of the designed CMfg network, a new objective function is developed. An ε-constraint augmented (AUGMECON2) method is used to solve the bi-objective model. Furthermore, a simple maximum-likelihood sampling (MLS) method is used to reduce the number of possible scenarios. Finally, some in-depth analyses are carried out to illustrate and validate the performance of the proposed solution approach.

Keywords: Modeling, simulation, control and monitoring of manufacturing processes, Design and reconfiguration of manufacturing systems, Industry 4.0
Abstract: In large engineering projects, multiplicity and heterogeneity of business actors, domain and business constraints, and stakeholders’ needs become more and more difficult to manage and are even moving. To allow projects members to converge in confidence, reducing time, costs and avoiding risky situations due to errors, misinterpretations, or omission, Model Based System Engineering (MBSE) replaces today the classical documents centric engineering approach. It promotes modeling, models and data management principles that are largely used in several domains with success. This article focuses on Nuclear Infrastructure engineering projects. It intends to demonstrate the interest of deploying MBSE approach in this field, particularly concerning architectural solutions evaluation. Main principles and application results of an MBSE driven method called EVA-CIME are proposed, presenting some gains and perspectives.

Keywords: Industry 4.0, Smart manufacturing systems, Monitoring, diagnosis and maintenance of manufacturing systems
Abstract: While the Digital Twin is now well discussed in manufacturing, the research considering the Digital Twin of services engineering is still in its early stage. The Digital Twin is very focused on the manufacturing operations with gathering data from physical means and information technology. Yet, services are now features and core concerns of complex systems development. However, it is not much focused on services and above all workflow orchestration to deliver these services to user whether it is an internal operator, or a customer. The engineering of Digital Twin services is a complicated phase because of the complexity of interactions and heterogeneous nature of services. The simultaneous use of models and data (e.g., Model Based System Engineering (MBSE)) should be considered for service-oriented engineering projects of complex system (large system, heterogeneous components, autonomous, etc.). Based on this postulate, the contribution of this paper is about Digital Twin and MBSE for Servitization industry. In detail, the paper proposes a recall of information systems for enterprises, workflow and servitization. Then it draws some perspectives about the interest of Digital Twin approach based on models for product service systems. It discusses the relation between service twin and ground data and the link to decision level.

Keywords: Knowledge management in production, Enterprise modelling, integration and networking, Modeling, simulation, control and monitoring of manufacturing processes
Abstract: Data, Information and Knowledge (DIK) problematics are of undeniable growing actuality; many efforts were made to explore and make progress in this domain. Therefore, these DIK have been defined by several characteristics conventionally studied such as volume, variety, variability… and a few solutions have been uncovered and revealed. Particularly, the SE and specifically MBSE approaches, that encourage, among other things, the use of models instead of documents in critical infrastructure engineering, participate in the growing volume and complexity of DIK. In this particular context, it is important to enrich MBSE by adapting the existing DIK advances to the MBSE needs, which is initiated in this paper, first by providing DIK definition, second, by checking what are the main issues that shall be solved in order to help stakeholders use and manage them in large MBSE-driven projects, involving various business actors during more or less long periods. At last, this article proposes a methodological contribution bridging the supposed gap between MBSE and so-called data management fields of research.

Keywords: Monitoring, diagnosis and maintenance of manufacturing systems, Modeling, simulation, control and monitoring of manufacturing processes, Quality management
Abstract: This paper presents an ensemble deep learning (DL) based structural health monitoring approach for complex systems. The proposed methodology consists of crack detection and crack growth prediction. The main objective in crack detection phase, is to detect either an input image has a crack pattern or not. An ensemble DL-based image segmentation technique, which is ResNet -UNet, has been developed to detect the existence of a crack pattern. The ensemble technique has very good performance in image classification, object detection and image segmentation problems. Once a crack has been detected from the image, the same image is put forward into crack length extraction phase. This phase measures the crack length and extracts the crack length feature for prediction. Two type of crack length measurement have been utilized in this phase, namely pixel-wise and bounding-box based measurements. The pixel-wise crack length extraction technique tries to extract crack length via counting the binary pixel values corresponding to the crack region. Whereas the bounding box technique puts a bounding box onto the crack region and calculates the crack length information. The crack length information extracted from both measurements are presented in comparative analysis section. ARIMA time series forecasting model is then trained on crack length feature to estimate remaining-useful-life (RUL) of crack surface. The proposed approach has been validated on different datasets. The result of proposed approach is very promising in structural health monitoring of complex systems.

Keywords: Design and reconfiguration of manufacturing systems, Robustness analysis, Transportation Systems
Abstract: Requirements engineering is a critical activity in developing complex cyber-physical systems. Requirements are usually expressed using natural language, which may be ambiguous, inconsistent, or incomplete. These issues in requirements qualities may introduce errors in system design that lead to high project cost overruns. Hence it is essential to verify the qualities of requirements early.

Since formal methods have demonstrated their ability to verify system designs and are increasingly adopted to support requirements engineering for software systems, a question arises about adapting formal methods to account for specific properties of cyber-physical systems.

Even if there are many literature reviews concerning requirements engineering, there is a lack of a global view on the reviews that specifically address the issues related to validation and verification (V&V) of requirements.

This paper aims to provide an overview of literature reviews related to requirements V&V and mainly investigates the use of formal approaches and models for preventing, detecting, or correcting errors occurring in requirements and identifies the main challenges of adopting formal methods on requirements engineering for cyber-physical systems.

Keywords: Business Process Modeling
Abstract: Search analytics and web data is widely used by media, politicians, economists, and scientists in various decision-making processes because it offers new opportunities to improve economic and demographic insights, and complement traditional data sources. In this paper, we explore the potentiel of Google trends data as a valuable alternative data source to forecast and nowcast Gross Domestic Product (GDP) growth in Morocco. The method we follow consists of constructing a Google trends index and using it to improve an auto-regressive model for forecasting and nowcasting GDP growth. The study finds that indeed the addition of an Internet search index improves GDP growth forecasting. In the following pages, we discuss the reasons for the varied success and potential avenues for future research.

Keywords: Decision Support System, Modeling, simulation, control and monitoring of manufacturing processes, Optimisation Methods and Simulation Tools
Abstract: In project management it is critical to obtain accurate cost forecasts using effective methods. This study presents a Machine Learning model based on Long-Short Term Memory to forecast the project cost. The model uses the seven-dimensional feature vector, including schedule and cost performance factors and their moving averages as a predictor. Based on the cost variation patterns from the training phase, we validate the model using three hundred experiments in the testing phase. Overall, the proposed model produces more accurate cost estimates when compared to the traditional Earned Value Management index-based model.

Keywords: Human-Automation Integration
Abstract: Artificial intelligence (AI) technologies have become increasingly common in creative practices in recent years. The rising number of research initiatives that emerge at the intersection of AI and art prompts researchers and artists to analyze the creative and explorative applications of AI in the context of art. First, this paper describes a specific AI art piece, an AI-generated music video for a song called Initiation, illustrating how AI can be used for creative purposes. This art piece was created in the context of the opening of Esch2022, the European Capital of Culture. Then, the paper provides an overview of the potential implications of AI technologies on the general understanding and creation of art.

Keywords: Human-Automation Integration, Decision Support System, Optimization and Control
Abstract: Nowadays, evolving technologies have contributed significantly to enriching the field of agriculture. The automation process is integrated to drive devices to work independently and communicate by including smart technologies and devices with which a multitude of tasks are executed without a human hand. Thus, this work introduces an automatic irrigation system based on smart sensors that can be used in a moderate and economic way to monitor the mint or any kind of plant by integrating some connected electronic devices and other advantageous instruments widely used in the field of IoT. This system includes a soil moisture sensor placed in the root zone of the plant, a temperature sensor, and a water flow sensor connected to the valve of the water pumping motor. These sensors are integrated with an Arduino UNO microcontroller, relay module, DC pumping motor, and power battery. In other words, the behavior of this automated system is encapsulated in detecting the soil moisture and the temperature level and automatically switching the pumping motor to ON or OFF in relation to the soil moisture state at a controlled timing. The sensed data is transmitted to a computer to be included in the CSV dataset from which graphs are generated for analysis during one day of recording. Generally, this kind of automated irrigation system could be easily applied to small gardens, nurseries, or greenhouses. Recently, innovative solutions have been incorporated for reducing costs, saving time, and optimizing the use of resources.

Keywords: Supply chains and networks, Supply Chain Management, Distributed systems and multi-agents technologies
Abstract: The advancement in technology, such as, Smart Logistics, IoT, RFID, sensors, and 5G, resulted in the evolution of Industry 4.0 that has started gaining a lot of popularity among different sectors like last mile delivery. This is important as the rising demand for such technology enabled platforms has been found to be necessary for fulfilling the opt for e-commerce services to support the retail outlets. The literature shows that to relax the pressure on the last mile sector, blockchain technology can be an effective solution both to protect the firm financial aspects and sustainability requirements. To ensure efficiency in the system and success in the implementation of blockchain technology into the last mile delivery sector, it is essential to study the various factors and capabilities of blockchain to handle the existing problems and requirements to analyze the efficiency of this integration. The focus areas of this paper are mainly to identify the impact of applying blockchain technology to support the last mile delivery of goods. The impacted areas focus mainly on the efficiency of the process and its leverage on the costs, both administrative and operational, and level of sustainability achieved. The proposed platform has enabled the enhancement of the integration of blockchain into the last mile delivery. The proposed smart contract system is designed to efficiently assign the orders from the demander to the respective fleet providers with the help of miners. This assignment is made possible by considering the various aspects that have been stored into the system, namely geographical location, the proximity to the destination of delivery along the route of delivery, size of the parcels, and capacity of the fleet.

Keywords: Sustainable Manufacturing, Facility planning and materials handling, Decision Support System
Abstract: In many cities and their surrounding environments, materials are wasted and underutilized. As a result, waste and energy recovery can play a critical role. The concept of circularity can be applied to urban environments and their areas of influence in order to stimulate Urban Industrial Symbiosis (UIS). As such, it is vital for cities to have a robust and trustworthy information management infrastructure. A blockchain-based system can provide the accountability the UIS networks need to become traceable, transparent and unchangeable for all the parties involved. Similar to the benefits that blockchain can provide supply chain networks, UIS could benefit from blockchain as well. In this article is addressed how blockchain and Smart Contracts can benefit UIS, what its implications are, and what the barriers and limitations are for its implementation, as well as the potential benefits and limitations of this new technology for UIS are discussed. Finally, through the implementation of modular blockchain architecture, an UIS blockchain model based on smart contracts is proposed.

Keywords: Blockchain, Urban Industrial Symbiosis, Smart Contracts, Distributed Ledger Technology, Eco-Industrial Parks

Keywords: Inventory control, production planning and scheduling, Production planning and scheduling, Supply chains and networks
Abstract: In a complex and changing market environment, enterprises recognize the benefit of collaborating with their suppliers and B2B customers to minimize risks and increase the agility and efficiency of the overall supply chain. To ensure this collaboration, they are asked to share real-time data related to their activities (stock, capacity, etc.) and to collaborate with their partners to optimize operation management, to reduce costs and reach optimal service level. Therefore, they require robust and reliable IT platforms allowing partners to exchange sensitive information and guarantee transparency but also security. That explains the increasing interest among supply chain practitioners and academicians toward the blockchain technology thanks to what it promises in terms of security, transparency and automation. In this paper, we present initial steps of a study aiming to develop, test and deploy a supply chain collaboration platform using the blockchain technology. The purpose of our study is to present answers to many unsolved questions related to the ability of the blockchain technology to manage real scale supply chain data and maintain the promised advantages.