4.7 Article

Logic-based Benders decomposition method for the seru scheduling problem with sequence-dependent setup time and DeJong's learning effect

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 297, 期 3, 页码 866-877

出版社

ELSEVIER
DOI: 10.1016/j.ejor.2021.06.017

关键词

Scheduling; Seru production system; Decomposition; Sequence-dependent setup time; Learning effect

资金

  1. National Natural Science Foundation of China [71401075, 71801129, 71871175]
  2. Natural Science Foundation of Jiangsu Province [BK20180452]
  3. Fundamental Research Funds for the Cen-tral Universities [30920 010 021]

向作者/读者索取更多资源

This paper focuses on scheduling problems in Seru production system, taking into account sequence-dependent setup time and learning effects. By reformulating the problem using LBBD method, high-quality schedules for Seru scheduling problems are achieved through a set partitioning master problem and independent subproblems. Computational studies demonstrate the effectiveness of the LBBD method in returning optimal schedules.
This paper concentrates on the scheduling problem in seru production system (SPS), where seru is a successful new-type production mode arising from the Japanese labor-intensive electronic assembly industry. Motivated by the practical situations, the sequence-dependent setup time and DeJong's learning effect are considered in seru scheduling problems, and the objective is to minimize the makespan. The seru scheduling problem is formulated as a mixed-integer programming (MIP), and then reformulated to a set partitioning master problem and some independent subproblems by employing the logic-based Benders decomposition (LBBD) method. Subsequently, the set partitioning master problem is used to assign jobs to serus of SPS, and the subproblems are applied to find the optimal schedules in each seru given the assignment of the master problem. Finally, computational studies are made, and results indicate that the LBBD method is able to return high-quality schedules for solving seru scheduling problems. (c) 2021 Elsevier B.V. All rights reserved.

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