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Journal of Shanghai University >

2025 , Vol. 31 >Issue 6: 945 - 960

DOI: https://doi.org/10.12066/j.issn.1007-2861.2677

Management Engineering

Joint optimization of assembly line balancing and part feeding strategy

  • LI Ranran ,
  • LI Xuejiao ,
  • PENG Yunfang ,
  • ZHU Haiping
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  • 1. Shanghai Aircraft Manufacturing Co., Ltd., Shanghai 201324, China;
    2. School of Management, Shanghai University, Shanghai 200444, China;
    3. School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Received date: 2025-02-20

  Online published: 2025-12-31

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Abstract

With increasing market competition and diversified customer demands, enterprises need to adopt flexible production methods to improve assembly efficiency and reduce production costs. Consequently, mixed-model assembly lines emerged in response to these challenges. Assembly line balancing and part feeding strategy are two critical decisions for ensuring an efficient assembly system. However, previous researches generally considered these problems separately, leading to an increase in total costs. Therefore, a mixed-integer programming (MIP) model is proposed for the joint optimization of assembly line balancing and part feeding strategy. The goal is to minimize the total costs of the assembly system, including assembly line costs, supermarket costs, and transportation costs. The model is validated through numerical examples and compared with the hierarchical model, confirming the necessity of the joint optimization approach. Sensitivity analysis shows that total costs decrease with increases in line-side space and kitting box size. However, constrained by factors such as cycle time, task allocation and part feeding strategy decisions will eventually stabilize.

Key words： assembly line balancing; part feeding strategy; joint optimization; mixed-integer programming model

Cite this article

LI Ranran , LI Xuejiao , PENG Yunfang , ZHU Haiping . Joint optimization of assembly line balancing and part feeding strategy[J]. Journal of Shanghai University, 2025 , 31(6) : 945 -960 . DOI: 10.12066/j.issn.1007-2861.2677

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