Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

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Reconfigurable manufacturing systems: Principles, design, and future trends

Reconfigurable manufacturing systems: Literature review and research trend

Recent advances in prognostics and health management for advanced manufacturing paradigms

Reconfigurable manufacturing systems (RMS) are considered the future of manufacturing, being able to overcome both dedicated (DMS) and flexible manufacturing systems (FMS). In fact, they provide significant cost and time reductions in the launch of new products, and in the integration of new manufacturing processes into existing systems. The goals of RMS design are the extension of the production variety, the adaption to rapid changes in the market demand, and the minimization of the investment costs. Despite the interest of many authors, the debate on RMS is still open due to the lack of practical applications. This work is a review of the state-of-the-art on the design of cellular RMS, compared to DMS, by means of optimization. The problem addressed belongs to the NP-Hard family of combinatorial problem. The focus is on non-exact meta-heuristic and artificial intelligence methods, since these have been proven to be effective and robust in solving complex manufacturing design problems. A wide investigation on the most recurrent techniques in DMS and RMS literature is performed at first. A critical analysis over these techniques is given in the end.

A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems

Explaining the Impact of Reconfigurable Manufacturing Systems on Environmental Performance: the role of top management and organizational culture

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

Integrated process planning and scheduling (IPPS) is a manufacturing strategy that considers process planning and scheduling as an integrated function rather than two separated functions performed sequentially. In this paper, we propose a new heuristic to IPPS problem for reconfigurable manufacturing systems (RMS). An RMS consists mainly of reconfigurable machine tools (RMTs), each with multiple configurations, and can perform different operations with different capacities. The proposed heuristic takes into account the multi-configuration nature of machines to integrate both process planning and scheduling. To illustrate the applicability and the efficiency of the proposed heuristic, a numerical example is presented where the heuristic is compared to a classical sequential process planning and scheduling strategy using a discrete-event simulation framework. The results show an advantage of the proposed heuristic over the sequential process planning and scheduling strategy.

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Process Plan Generation in Reconfigurable Manufacturing Systems using AMOSA and TOPSIS

A Simulation-based Genetic Algorithm Approach for Process Plans Selection in Uncertain Reconfigurable Environment

Reconfigurable manufacturing system (RMS) is a manufacturing paradigm that cost-effectively responses to market changes. This paper addresses the multi-objective process plan generation problem in RMS. More specifically, two meta-heuristics, namely Non-dominated Sorting Genetic Algorithm (NSGA-II) and Archived Multi-Objective Simulated Annealing (AMOSA), are adapted to generate near-optimal process plans. With the help of a numerical example, the performances of the two metaheuristics are demonstrated and compared.

Abderrahmane Bensmaine

Papers

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

A new heuristic for integrated process planning and scheduling in reconfigurable manufacturing systems

Process Plan Generation in Reconfigurable Manufacturing Systems using AMOSA and TOPSIS

A Simulation-based Genetic Algorithm Approach for Process Plans Selection in Uncertain Reconfigurable Environment

Process plan generation in reconfigurable manufacturing systems using adapted NSGA-II and AMOSA