A three-layer chromosome genetic algorithm for multi-cell scheduling with flexible routes and machine sharing

Yanling Feng, Guo Li and Suresh Sethi

International Journal of Production Economics, 2018, vol. 196, issue C, 269-283

Abstract: Alternative machines assignment, machine sharing, and inter-cell movements are very common yet difficult to be solved integratedly in modern dynamic Cellular Manufacturing Systems (CMS). In this paper, we incorporate these issues and consider a dynamic cellular scheduling problem with flexible routes and machine sharing. We employ a mixed integer programming scheduling model to minimize both the makespan and the total workload. To solve this new model, we propose a three-layer chromosome genetic algorithm (TCGA). We first compare the performances of the proposed TCGA with the optimal solution obtained by CPLEX. Computational results show that the TCGA performs well within a reasonable amount of time. We further compare our proposed TCGA with the classic genetic algorithm (GA) and the shortest processing time (SPT) rule through numerical experiments. The results reveal that the TCGA significantly improves the performance and effectively balances the workload of machines.

Keywords: Dynamic cellular manufacturing; Inter-cell movement; Bi-objective programming; Three-layer chromosome genetic algorithm; Machine sharing (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:196:y:2018:i:c:p:269-283

DOI: 10.1016/j.ijpe.2017.12.003

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