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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China
出 版 物:《KNOWLEDGE-BASED SYSTEMS》 (知识库系统)
年 卷 期:2023年第279卷
核心收录:
学科分类:08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:National Natural Science Foun-dation of China [51875421 51875420]
主 题:Mixed-model assembly job-shop scheduling Batch transfer Gene expression programming Clustering Simulated annealing
摘 要:In the mixed-model assembly job-shops, all the components are transferred in batches from the processing stage to the assembly stage to reduce transportation costs and timely satisfy the assembly material requirements. However, batch transfer in such a workshop has not received due attention in the current literature. Thus, this work addresses the mixed-model assembly job-shop scheduling problem with batch transfer via a mathematical model and an augmented simulated annealing algorithm to minimize manufacturing and transportation costs. The mathematical model takes batch transfer constraints into account and constructs the temporal and spatial links among the processing, transfer, and assembly stages. Production sequencing knowledge and batch transfer knowledge are first extracted via gene expression programming algorithm and clustering method and then implanted into the algorithmic operators in the form of rules and strategies to directly identify the promising solution space. Besides, eleven neighborhood structures including the critical component-based and batch based ones, are designed to enhance the exploitation ability. A restart mechanism considering crossover and tempering helps to get out of local optima. Experimental results indicate that by adopting the extracted knowledge, problem-specific neighborhood structures, and restart mechanism, the proposed algorithm significantly outperforms other comparison algorithms in fixing the studied problem.(c) 2023 Published by Elsevier B.V.