文献类型：期刊文献

英文题名：A modified adaptive switching-based many-objective evolutionary algorithm for distributed heterogeneous flowshop scheduling with lot-streaming

作者：Chen, Sanyan[1];Wang, Xuewu[1];Wang, Ye[1];Gu, Xingsheng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：81

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20232714357275);WOS:【SCI-EXPANDED(收录号:WOS:001034831800001)】；

基金：Acknowledgments This work is supported by the National Natural Science Foundation of China (Grant Nos. 61973120 and 62076095) .

语种：英文

外文关键词：Distributed heterogeneous flowshop scheduling; Lot-streaming; Many-objective optimization; Collaborative search; Adaptive switching strategy

摘要：The distributed heterogeneous permutation flowshop scheduling problem with lot-streaming (DHPFSPLS) is a provocative scheduling and optimization problem confronting both industry and engineering. However, no result is available in investigating the DHPFSPLS with variable number of sublots. This paper presents a many-objective mathematical model of this problem with the objectives of makespan, idle time of machines, total production cost and total flow time, considering the transfer time and sequence-independent setup time. Based on this model, a modified adaptive switching-based many-objective evolutionary algorithm is proposed, in which each solution is coded using a three-vector-based solution representation, i.e., a factory assignment vector, a lot-splitting vector and a job permutation vector. Then, a novel two-population collaborative search strategy based on a learning mechanism is designed, which can enhance exploitation abilities and make effective use of optimization knowledge from the population. Moreover, an adaptive switching strategy-based environmental selection is implemented to ensure the convergence and diversity of the solution set. Through a variety of computational tests and comparisons, the effectiveness of the proposed algorithm in solving the many-objective DHPFSPLS is demonstrated.