Decomposition-based multi-objective approach for a green hybrid flowshop rescheduling problem with consistent sublots

Weiwei Wang, Biao Zhang, Xuchu Jiang, Baoxian Jia, Hongyan Sang and Leilei Meng

International Journal of Production Research, 2024, vol. 62, issue 21, 7904-7932

Abstract: Rescheduling in a hybrid flowshop holds significant importance in modern industries that face uncertain events. Moreover, real-world manufacturing scenarios often utilise lot streaming to enhance market competitiveness. In light of escalating energy demands and their consequential environmental impacts, contemporary manufacturing companies are placing a heightened emphasis on energy efficiency. This study addressed a green hybrid flowshop rescheduling problem with consistent sublots (GHFRP_CS) in the context of urgent lot insertion. Initially, we establish an optimisation model aimed at minimising the makespan, total energy consumption, and system stability. To tackle this NP-hard multi-objective optimization problem, we develop a constructive heuristic generating promising solutions based on lot split, sequence, and local search rules. Further improvement is achieved through a multi-objective discrete artificial bee colony algorithm (MDABC). MDABC decomposes the problem into sub-problems, initiating solutions with the constructive heuristic and refining them through employed bee, onlooker bee, and scout bee phases. Computational experiments compare MDABC with other multi-objective evolutionary algorithms (MOEAs) on small- and large-scale problems. Results demonstrate MDABC's superiority, achieving fourfold accuracy and efficiency enhancement for small-scale instances and sixfold improvement for large-scale problems at low cost compared to other MOEAs.

Date: 2024
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DOI: 10.1080/00207543.2024.2333943

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