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The reconfigurable manufacturing system (RMS) is a recent manufacturing paradigm driven by the high responsiveness and performance efficiencies. In such system, machines, material handling units or machines components can be added, modified, removed or interchanged as needed. Hence, the design of RMS is based on reconfigurable machines capabilities and product specification. This paper addresses the problem of machines selections for RMS design under unavailability constraints and aims to develop an approach to ensure the best process plan according to the customised flexibility required to produce all parts of a given product. More specifically, we develop a flexibility-based multi-objective approach using an adapted version of the well-known non-dominated sorting genetic algorithm to select adequate machines from a set of candidate (potential) ones, in order to ensure the best responsiveness of the designed system in case of unavailability of one of the selected machines. The responsiveness is based on the flexibility of the designed system and a generated process plan, which guarantees the management of machines unavailability. It is defined as the ability and the capacity to adapt the process plan in response to machines unavailability. Two objectives are considered, respectively, the maximisation of the flexibility index of the system and the minimisation of the total completion time. To choose the best solution in the Pareto front, a multi-objective decision-making method called technique for order of preference by similarity to ideal solution is used. To demonstrate the applicability of the proposed approach, a simple example is presented and the numerical results are analysed. [ABSTRACT FROM PUBLISHER]

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]

In a competitive and maintainability context, each company looks for optimizing its supply chain in order to satisfy its customers by providing the best quality products in the best delays and with the lost costs. In this sense, we are interested in a single- commodity stochastic supply chain design problem. Our supply chain is composed of suppliers and retailers. The objective is to find the best location for distribution centres (DCs) and to serve retailers from suppliers through DCs in a random supply lead time. We presented a non-linear optimization model that integrates the selection of suppliers, the location of DCs, and the retailers' allocation decisions with an oriented cost function to minimize. Note that the determination of exact solutions to this problem is a NP-hard problem. Accordingly, we proposed an optimization approach using three different metaheuristics: genetic algorithm, simulated annealing, and taboo search to solve this problem and find the best supply chain structure (location of DCs, allocation of suppliers to DCs and DCs to retailers). Computational results are presented and compared to evaluate the efficiency of the proposed approaches. [ABSTRACT FROM AUTHOR]

A supply chain is a set of facilities connected together in order to provide products to customers. The supply chain is subject to random failures caused by different factors which cause the unavailability of some sites. Given the current economic context, the management of these unavailability is becoming a strategic choice to ensure the desired reliability and availability levels of the different supply chain facilities. In this work, we treat two problems related to the field of supply chain, namely the design and unavailability management of logistics facilities. Specifically, we consider a stochastic distribution network with consideration of suppliers' selection; distribution centers location (DCs) decisions and DCs' unavailability management. Two resolution approaches are proposed. The first one called non-integrated approach consists of defining the optimal supply chain structure using an optimization approach based on genetic algorithms (GA), then to simulate the supply chain performance in the presence of DCs failures. The second approach called integrated approach is to consider the design of the supply chain problem and unavailability management of DCs in the same model. Note that, we replace each unavailable DC by performing a reallocation using GA in the two approaches. The obtained results of the approaches are detailed and compared to show their effectiveness. [ABSTRACT FROM AUTHOR]

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