Integrated Optimization of Mixed-Model Assembly Line Balancing and Buffer Allocation Based on Operation Time Complexity

Liu, Xuemei; Lei, Mingliang; Zeng, Qingfei; Li, Aiping

doi:10.1016/j.procir.2019.03.248

Cited by 5 publications

(1 citation statement)

References 11 publications

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“…21,22 In general, there are two main types of ALBP: (1) simple assembly line balancing problems (SALBPs) in which assembly lines are arranged as serial lines in a sequential pattern with all input parameters, without considering uncertainty 23,24 ; (2) generalized assembly line balancing problems which include all issues not addressed in SALBPs and display more constraints than SALBPs. 25 In the recent years, the extensions of ALBP have been conducted by researchers including mixed-model ALBP, 26,27 stochastic assembly line balancing with learning effects, 28 type-1 U-shaped assembly line balancing under uncertain task time, 29 two-sided ALBP, 30 reconfigurable transfer line balancing problem, 31 robotic ALBP, 32 robust ALBPs, 33 simple type-1 ALBPs, 34 flexible multimanned ALBP, 35 cost-oriented resource-constrained multimodel ALBP, 36 multimanned ALPB with skilled workers, 37 and disassembly line balancing problem. 38 Published literature shows that the scope of the ALBP in research is indeed quite clear, with well-defined sets of assumptions, parameters, and objective functions.…”

Section: Assembly Line Balancingmentioning

confidence: 99%

A complex garment assembly line balancing using simulation‐based optimization

Bongomin

Mwasiagi

Nganyi

et al. 2020

Engineering Reports

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The nascent wave of disruptive competition in the current business environment brought about by the fourth industrial revolution (Fashion 4.0 or Apparel 4.0) is enormous. Therefore, it is important for the apparel industry to be flexible enough to respond quickly to the unstable customers' demand through continuous improvement of their process efficiency and productivity. This study proposed assembly line balancing problem (ALBP) for complex garment assembly line using simulation-based optimization under stochastic task times. The proposed ALBP solution approach aimed at minimizing the cycle time for a given number of workstations with consideration of constraints on number of resources, precedence relations, and resource types. The empirical study was conducted at Southern Range Nyanza Limited (NYTIL) garment facility and a complex trouser assembly line with 69 workstations was considered. The discrete event simulation of the trouser assembly line was developed using Arena simulation software. The local optimal solution was obtained from simulation experiments which was adopted for the optimization process. The OptQuest tool was used to solve a single objective optimization with discrete control values. The results showed that the average throughput increased by 30% for local optimal line balancing and 55% for global optimal line balancing. Consequently, the cycle time reduced by 23% and 36%, respectively.

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