A hybrid imperialist competitive algorithm for energy-efficient flexible job shop scheduling problem with variable-size sublots. (October 2022)

• Record Type: Journal Article
• Authors: Li, Yibing; Yang, Zipeng; Wang, Lei; Tang, Hongtao; Sun, Libo; Guo, Shunsheng
• Is Part Of: Computers & industrial engineering. Volume 172:Part B(2022)
• Publication Date: 2022-10
• Subjects: Flexible job shop scheduling problem -- Energy consumption -- Variable-size sublots -- Continuous searching strategy -- Imperialist competitive algorithm -- Simulated annealing algorithm; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108641
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: The mathematical model of EFJSP with variable-size sublots is formulated. A mapping strategy and the CSR are developed to limit the searching space. The HICSA based on the imperialist competitive algorithm is proposed. The performance of HICSA is better than comparative algorithms. Abstract: Variable-size batching method has a higher possibility to obtain the most flexible scheduling schemes, which can be better applied to the manufacturing process. However, changing the sizes and number of sublots will not only increase the energy consumption of the manufacturing process, but also increase the search space of the scheduling schemes. A multi-objective optimization problem is formulated considering the makespan and total energy consumption simultaneously. Then, a two-stage multi-objective hybrid algorithm (HICSA) combining imperialist competitive algorithm (ICA) and simulated annealing algorithm (SAA) is proposed to solve this problem and improve the searching efficiency. The ICA and SAA are used to search for the suitable job sequences with optimal machine assignment and the optimal lot splitting schemes in the two stages, respectively. In order to improve the search efficiency, a novel mapping strategy that turns the discrete space into continuous space is applied. Extensive experiments are conducted and the computational results show that HICSA provides promising results for the problem.