The performance of priority dispatching rules in a complex job shop: A study on the Upper Mississippi River

Abstract Many studies have derived and tested dynamic job shop priority dispatching rules using discrete event simulation models in the context of idealized job shop experimental designs. This paper extends research on evaluating priority dispatching rules in a completely reactive dynamic job shop by testing the performance of eight selected rules in a simulation model of a complex and real dynamic job shop: the Upper Mississippi River Inland Navigation Transportation System (UMR). The UMR incorporates many real-world complexities such as sequence dependent and seasonally varying stochastic job processing times, both capacitated and un-capacitated servers, and heterogeneous jobs with seasonally varying, interdependent stochastic arrivals that can balk (opt-out) at using the system in response to anticipated poor levels of service. Employing two related but different metrics, mean flow times and the total value (“utility”) of jobs processed, the results show that rules that incorporate increasingly more systemic information generally perform better as system congestion increases, particularly when balking is not allowed. However, this is not the case when customer balking is allowed, particularly for value-based priority dispatching rules. This demonstrates that the balking decision of system users has a large impact on the performance of the rules and the expected utility (value) generated by the system, particularly at high levels of system congestion.