A COMPREHENSIVE ANALYSIS OF GROUP SCHEDULING HEURISTICS IN A JOB-SHOP CELL

This paper describes a broad-based simulation study of the performance of two-stage group scheduling heuristics in a job shop cell. The objective of this study was to examine the direct and interactive effects of a variety of shop factors on the performance of the best, previously reported, group scheduling heuristics. A set of traditional single-stage scheduling heuristics were examined as well. Shop factors considered include: setup to runtime ratio, cell load level and variability of inter-arrival times. An assumption common to group scheduling research which provides for an equal division of the part family into subfamilies is also examined. This is accomplished through the creation of an alternative scenario where the majority of the parts are assigned to one subfamily, i.e. one subfamily dominates the part family population. The effects of set up to runtime ratio and cell load have been examined in previous group scheduling research, but not in conjunction with the inter-arrival time variability factor. Further, no study has examined the impact of subfamily dominance on group scheduling heuristics in a full-scale simulation study. The results indicate that performance comparable to that of the two-stage heuristics can be obtained with the easily implementable single-stage heuristics when factors which lessen the impact of setup times are in place. In particular, the tardiness performance of two-stage scheduling heuristics deteriorates when subfamily dominance is in effect while the single-stage heuristics exhibit dramatic improvements in tardiness performance. Low setup to runtime ratio, shop load, and less variable inter-arrivals all induce dramatic performance gains across all measures among the single-stage heuristics, while yielding only marginal improvement in the performance of the two-stage heuristics. As a result, in many instances when combinations of these factors are in effect, the single-stage heuristics yield similar performance to the two-stage heuristics.