Studying the Effects of Operational Factors on the Performance of Line Balancing Heuristics for Solving SALBP-1

A great deal of work has been directed to assembly line balancing (ALB) using different heuristics. However, little work is made on evaluating the performance of ALB heuristics under various operating factors. In this paper, the performance of common heuristics in solving the single-model assembly line balancing problem (SLABP-1) is investigated using a dataset of randomly generated ALB problems. The dataset is sorted to obtain representative levels of cycle time, time variability (TV), number of tasks, and order strength. An experiment is then designed to investigate the effects of factors on the performance of heuristics with a focus on line efficiency, largest workstation idle time, smoothness index, total idle time, number of workstations, and west ratio. In this study, the optimal working settings for the balancing process are determined based on the performance metrics achieved. The results show that several heuristics can optimize multiple performance metrics given specific factor settings. Furthermore, findings reveal that some performance measures can be optimized at the same factor levels regardless of the heuristics used. TV is recognized to be a critical factor to ALB problem, with lower TV achieving better performance measures. Process designers should understand the impact of factors effects on choosing the ALB heuristic. A heuristic, which can optimize multiple performance measures simultaneously, is recommend.

Automated summary

This study investigates the performance of common heuristics in solving the single-model assembly line balancing problem. A dataset of randomly generated problems is used to assess the performance, and an experiment is conducted to analyze the effects of various operating factors. The results show that certain heuristics can optimize multiple performance metrics, and time variability is identified as a critical factor influencing the performance. Process designers are recommended to consider the impact of operating factors and use heuristics that can optimize multiple performance measures.