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A reconfigurable manufacturing system (RMS) is one of the next generation production systems widely used to meet uncertain market demands in the context of Industry 4.0. The design of the RMS aims to achieve sufficient responsiveness so that it can be quickly adopted to the changes required for a niche market of a customized product family. Most components of the RMS are designed to be modular. Reconfigurable machines are one of the main modular components of the RMS. In the design of the RMS, the problem of machine selection is of primary interest, as the modular machines, with their respective tools and configurations, are selected to perform a given part from the part family. Due to this trilogy of machine, tool, and configuration selection, only one type of machine is considered. To remedy this shortcoming, this work introduces a new concept of modular machine configuration capability, which leads to the selection of two types of machines, namely, the single-spindle modular reconfigurable machines (SRMT) and the multi-spindle reconfigurable machines (MRMT). This paper addresses the problem of machine selection and RMS design. Firstly, a bi-objective mathematical model is developed for the generation of the process plan and the selection of reconfigurable machines. The results obtained, together with an initial layout, are then used to generate the RMS design. Secondly, a new objective function is introduced to address the problem of under-utilization of reconfigurable multi-spindle machines. A NSGA-III (non-dominating sorting genetic algorithm III)-based approach is proposed to solve the proposed models. To help the decision maker, the pseudo-weight technique is used to determine the best process plan and the best machines to include in the new designed RMS. [ABSTRACT FROM AUTHOR]

Reconfigurable manufacturing systems (RMS) are designed for adjustable production capabilities to cope with the fluctuating market demand. This adjustable capability and customised flexibility are offered by the modular Reconfigurable Machine Tools (RMTs), considered as the key component of an RMS. The main objective of this work is to develop a new approach to jointly consider the setup and process plan constraints. Indeed, based on the relationships between the operations to perform, a integrated setup and process plan is generated, minimising the total cost, including cost of processing, tolerance, setup change and tool module. The proposed new hybrid genetic algorithm-based approach is conducted in two stages. In the first stage, a heuristic is developed for the generation of setups and the assignments of fixtures to each set of operations. While in the second stage, a genetic algorithm is proposed to determine the best process plan to associate with the generated setup plan, under the economic cost consideration. A numerical experiment is performed to show the applicability and the efficiency of the developed approach. A test results highlight the economic gain of the simultaneous consideration of setup and process planning. [ABSTRACT FROM AUTHOR]

In a highly competitive environment, characterized by shorter product life cycles, mass customization and fluctuating markets, the supply chain network design is considered as one of the most important steps for reducing their costs, increasing their performance, competitiveness and profits. In addition, due to the growing social, governmental and industrial concerns about environmental protection and greenhouse gas reduction, policy makers are now forced to incorporate the environmental issue and sustainability constraints into their logistics network design process. The purpose of the present paper is to address the issue of sustainable supply-chain-network design by integrating simultaneously the strategic decisions related to the location of facilities with limited capacity and transport network design with capacity constraints on the links to be built, and constraints related to environmental protection. A multi-objective model is proposed to minimize the cost and environmental impact associated with transport activities by considering different environmental impacts of the links to be built. An adaptation of the Non-dominated Sorting Genetic Algorithm II, using mixed coding is developed to effectively solve the proposed model. In order to evaluate the performance of the proposed approach, a number of numerical experiments on instances of different sizes have been performed. Finally, a sensitivity analysis is presented to analyse the behaviour of the proposed model in response to changes in the key parameters of the problem. [ABSTRACT FROM AUTHOR]

Smartphones are nowadays one of the most used electronic devices in everyday life. They are known to have an average useful lifespan between 18 and 24 months. Indeed, users are led up to dispose of them at the end of their lifespan, and buy last generation smartphones, increasing thus the electronic wastes. To reduce the environmental issues, we propose in this paper to extend the useful lifespan of smartphones based on recovery and remanufacturing options. The developed remanufacturing policies aim to determine the best decisions to ensure the required level of smartphone performances, with the customer profiles and the recovery and remanufacturing center capacity. We propose a new heuristic which constructs a feasible plan for the recovery and the remanufacturing of used smartphones and determines the grade to achieve for each customer, minimising the economic and the environment generated costs. [ABSTRACT FROM AUTHOR]

With ever-increasing concerns about climate change, and in the context of diminishing fossil energy resources, energy consumption has become a subject of great importance. In this framework, our study is concerned with the reduction of the energy consumed by a manufacturing system composed of several unrelated parallel machines. We investigate the problem of scheduling a set of n deteriorating jobs on m parallel unrelated machines subject to a deterioration effect, where the processing time of each job is a linear deterioration function of the starting time. The objective is to determine the best scheduling of the jobs, minimizing the makespan and the total energy consumption. We implement a nondominated sorting-based multiobjective algorithm (NSGA-II)-based approaches with a given scheduling rules to solve the developed mathematical model of the considered problem. The performance of the proposed approaches are compared with an exact algorithm and evaluated through computational experiments. Then, we give a multiobjective decision-making method based on the TOPSIS (technique for order of preference by similarity to ideal solution) technique to determine the best solution from the Pareto front according to the decision-maker's preferences. [ABSTRACT FROM AUTHOR]

Routing flexibility is one of the most common types of flexibilities of manufacturing systems. It allows the system to continue producing given part types despite uncertainties. Its main purpose is to maintain a high level of performance so that the system can deal with disturbances (failures, maintenance actions,...). This type of flexibility occurs when there are alternative or redundant machine tools in the system. However, due to resource and alternative routing limitations, the scheduling problems in such systems can become very complex. Furthermore, though routing flexibility aims to enhance system responsiveness, it still depends on the reliability and availability of machines and individual components in a system. The present paper aims to investigate the scheduling problem in a flexible manufacturing system (FMS) with routing flexibility under uncertainties related to the random arrival of parts orders and machines failures, by considering reliability and maintenance constraints. The real-time decisions for part routing selection are made using a non-dominated sorting genetic algorithm (NSGA-II), by considering the workload, utilization level, and reliability of machines in a workstation, in order to minimize the deadlocks and maximize the overall system reliability. The simulation results obtained showed that, for an overloaded system, the proposed NSGA-II algorithm induces the best performance in terms of total profit, system productivity, and machines utilization. [ABSTRACT FROM AUTHOR]

In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework. [ABSTRACT FROM AUTHOR]

Enhancing productivity, reducing inaccuracy and avoiding time waste at changeover are considered major drivers in manufacturing system design. One of the emerging paradigms concerned with these characteristics is reconfigurable manufacturing systems (RMSs). The high responsiveness and performance efficiencies of RMS make it a convenient manufacturing paradigm for and flexible enabler of mass customization. The RMS offers customized flexibility and a variety of alternatives as features thanks to its reconfigurable machine tool (RMT). These machines represent a major component of RMS and are based on an adjustable, modular and reconfigurable structure. Hence, the system modularity is of great importance. This paper outlines a multi-objective approach to optimize the RMS design. Three objectives are considered: the maximization of the system modularity, the minimization of the system completion time and the minimization of the system cost. We developed a modularity-based multi-objective approach that uses an adapted version of the 'Archived Multi-Objective Simulated Annealing' (AMOSA) method to solve the optimization problem by selecting from a set of candidate machines the most suitable ones. Implemented, the decision maker can use a multi-objective decision making tool based on the well-known 'Technique for Order of Preference by Similarity to Ideal Solution' (TOPSIS) to choose the best solution in the Pareto front according to his preferences. We demonstrated the applicability of the proposed approach through an illustrative example and an analysis of the obtained numerical results. [ABSTRACT FROM AUTHOR]

Offshore wind farms are a growing source of energy, which aims to ensure a clean energy with a low environmental impact. In this context, this paper investigates opportunities of the turbine gearbox end of life-cycle to improve the operation and maintenance strategies. We determine the impact of spare part policy based on the remanufacturing of gearboxes recovered after each replacement. The remanufacturing implementation allows the extension of the gearbox life-cycle and involves a perfect organization and coordination between maintenance, monitoring, operation and spare part supply chain to determine the best way to use each gearbox of each wind turbine. In this paper, we present a multi-agent based approach to analyze the impact of the spare parts remanufacturing strategy on the performance of an offshore wind farm in term of total cost and carbon footprint. [ABSTRACT FROM AUTHOR]

This research investigates the case of a machine subject to a stochastic degradation. A condition-based replacement policy is adopted to keep the system running. The replacements are carried out with spare parts of a given quality determined by their degradation level. The replacement spare parts are either new or used: the used ones are the parts recovered from the machine at the time of previous replacements. Before the replacement, a remanufacturing process can be applied on the recovered part to improve its quality, by reducing its degradation level. The purpose of this paper is to propose an optimal maintenance policy, by determining dynamically (i.e. at each moment of decision making): the decision thresholds, inspection dates and spare parts qualities to be installed in the machine. Two components approach is proposed to achieve these objectives, the first component is executed offline and the second component is run online. Numerical examples are presented to illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The present work addresses a multi-period facility location decision during crisis—Pandemic—situation with highly uncertain demand. Our paper identifies failing distribution centres (DCs) over several periods of time and decides about relocation. Several scenarios arise: DCs can be opened, displaced from one location to another, closed or even reopened during a certain time, using mobile facility. We recommend a multi-objective non-linear mixed-integer mathematical model that identifies, for each period, the best possible location for DCs and their allocation to customers. Our model analyses the trade-off between economic costs and CO2\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$CO_2$$\end{document} emissions generated by facilities operations. We suggest a multi-objective approach based on a Non-dominated Sorting Genetic Algorithm II (NSGA-II). Our simulations demonstrate how mobile facility should be managed to ensure a good balance between economic and environmental criteria. [ABSTRACT FROM AUTHOR]