Routing distributions and their impact on dispatch rules

Abstract

Motivated by the job-shop production process of our industry partner, we examine dispatching rules effects on two key performance indicators (KPIs) – job lateness and the percentage of late jobs. In the literature, authors use the uniform distribution to generate random job shop data. In addition to our discussion on dispatching rules, we propose an alternative idea for random job shop data, the routing distribution, and we compare dispatching rules performance using KPI frontiers under different routing distributions. We show that using their current dispatch rule, earliest operation due date (EODD), the industry partner is never worse off, even as their job-shop’s operational environment changes. We further show that using multiple dispatch rules across several job-shop departments does improve a job-shop’s performance on the KPIs, though the improvement is small and in some cases may not be statistically significant. In addition, we find that EODD is one of several dispatching rule which consistently lie on the KPI frontier for different job routing distributions. We find that dispatching rule performance is greatly affected by the routing distribution of the job-shop where the rules are employed. Lastly, we leave the readers with some insight into determining which dispatch rules and routing distributions should be considered for different job shops.