Balancing and sequencing of mixed-model assembly line considering preventive maintenance scenarios: mathematical model and a migrating birds optimization algorithm

Kai Meng, Qiuhua Tang () and Zikai Zhang
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Kai Meng: Wuhan University of Science and Technology
Qiuhua Tang: Wuhan University of Science and Technology
Zikai Zhang: Wuhan University of Science and Technology

Flexible Services and Manufacturing Journal, 2023, vol. 35, issue 4, No 8, 1175-1205

Abstract: Abstract In the mixed-model assembly line balancing and sequencing problem (MALBSP), workstations are assumed to be constantly available. The failure of any workstation will make the entire assembly line stop working. Preventive maintenance (PM) is a way to maintain the workstation before its failure, reduce unexpected downtime, and prolong its useful life. Previous studies have considered PM scenarios (PMS) in the simple and U-shaped assembly line to improve production efficiency and smoothness effectively, but not in the mixed-model assembly line. This paper fills this research gap, and the MALBSP considering PMS (MALBSP_PMS) is studied in this paper. A mixed-integer linear programming model is proposed to minimize makespan and task alteration. A migrating birds optimization algorithm is improved (IMBO) to obtain well-distributed Pareto frontier solutions. This algorithm designs a restart mechanism and an intra-population crossover operator to avoid falling into the local optimal and enhance its searchability. Experimental results demonstrate the effectiveness of two improvements and the IMBO algorithm. In addition, a real-world case study is introduced to illustrate the importance of considering PM scenarios in MALBSP.

Keywords: Mixed-model assembly line; Balancing and sequencing; Preventive maintenance scenarios; Migrating birds optimization problem (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10696-022-09477-4

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