Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

Ming Liu 1 Lijie An 1 Jiantong Zhang 1 Feng Chu 2 Chengbin Chu 3
1 School of Economics and Management
2 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes
3 LGI - Laboratoire Génie Industriel - EA 2606
Abstract : This paper investigates a multi-module reconfigurable manufacturing system for multi-product manufacturing. The system consists of a rotary table and multiple machining modules (turrets and spindles). The production plan of the system is divided into the system design phase and the manufacturing phase, where the installation cost and the energy consumption cost correspond to the two phases, respectively. A mixed-integer programming model for a more general problem is presented. The objectives are to minimise the total cost and minimise the cycle time simultaneously. To solve the optimisation problem, the ε-constraint method is adopted to obtain the Pareto front for small size problems. Since the ε-constraint method is time consuming when problem size increases, we develop a multi-objective simulated annealing algorithm for practical size problems. To demonstrate the efficiency of the proposed algorithm, we compare it with a classic non-dominated sorting genetic algorithm. Experimental results demonstrate the efficiency of the multi-objective simulated annealing algorithm in terms of solution quality and computation time.
Keywords : Reconfigurable manufacturing system Bi-objective optimisation Energy consumption Multi-module Multi-product
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Journal articles
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https://hal.archives-ouvertes.fr/hal-02423767
Contributor : Frédéric Davesne <>
Submitted on : Wednesday, December 25, 2019 - 7:52:01 PM
Last modification on : Wednesday, January 22, 2020 - 11:39:32 AM

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UNIV-EVRY | IBISC | IBISC-AROBAS | CENTRALESUPELEC | LGI | UNIV-PARIS-SACLAY | LGI-MO

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Ming Liu, Lijie An, Jiantong Zhang, Feng Chu, Chengbin Chu. Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system. International Journal of Production Research, Taylor & Francis, 2019, 57 (19), pp.5974--5995. ⟨10.1080/00207543.2018.1556413⟩. ⟨hal-02423767⟩

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