Reconfigurability improvement in Industry 4.0: a hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment

Muhammad Ameer () and Mohammed Dahane ()
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Muhammad Ameer: Université de Lorraine
Mohammed Dahane: Université de Lorraine

Journal of Intelligent Manufacturing, 2023, vol. 34, issue 3, No 27, 1445-1467

Abstract: Abstract 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.

Keywords: Industry 4.0; Reconfigurable manufacturing system; Process planning; Setup planning; Reconfigurable machine tools; Hybrid optimisation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10845-021-01869-x

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