An evolutionary genetic algorithm for a multi-objective two-sided assembly line balancing problem: a case study of automotive manufacturing operations. Issue 1 (2nd January 2023)

• Record Type: Journal Article
• Authors: Kang, He-Yau; Lee, Amy H. I.
• Is Part Of: Quality technology & quantitative management. Volume 20:Issue 1(2023)
• Page Start: 66
• Page End: 88
• Publication Date: 2023-01-02
• Subjects: Assembly line balancing -- two-sided -- multi-objective linear programming (MOLP) -- genetic algorithm (GA); Quality control -- Periodicals; Quality control -- Statistical methods -- Periodicals; Industrial management -- Periodicals; Industrial management; Management -- Research -- Methodology -- Periodicals; Qualitative research -- Periodicals; Management; Quality control; Quality control -- Statistical methods; Periodicals; 658.00721
• DOI: 10.1080/16843703.2022.2079062
• Languages: English
• ISSNs: 1684-3703

Abstract

ABSTRACT: Assembly lines are often indispensable in factories, and a good assembly-line balancing model is very important for manufacturers to maximize their profit using limited resources in a competitive environment. To maintain a productive assembly line, multiple objectives with different importance must be considered at the same time. In this paper, a two-sided assembly-line balancing problem (TALBP) with multiple objectives is examined. A fuzzy multi-objective linear programming-weighted model (FMOLP-W) for solving the TALBP is constructed first with the consideration of the importance weights of the line balancing performance factors, including minimizing the number of workstations, minimizing cycle time, maximizing line efficiency, minimizing smoothness index and minimizing workstation idle time. An evolutionary genetic algorithm (GA) is proposed next to tackle large-scale problems when the problems are too complex to be solved by the FMOLP-W.