Investigating the Generality of Genetic Programming Based Hyper-heuristic Approach to Dynamic Job Shop Scheduling with Machine Breakdown

Abstract

Dynamic job shop scheduling (DJSS) problems are combinatorial optimisation problems that have been extensively studied in the literature due to their difficulty and their applicability to real-world manufacturing systems, e.g., car manufacturing systems. In a DJSS problem instance, jobs arrive on the shop floor to be processed on specific sequences of machines on the shop floor and unforeseen events such as dynamic job arrivals and machine breakdown occur that affect the properties of the shop floor. Many researchers have proposed genetic programming based hyper-heuristic (GP-HH) approaches to evolve high quality dispatching rules for DJSS problems with dynamic job arrivals, outperforming good man-made rules for the problems. However, no GP-HH approaches have been proposed for DJSS problems with dynamic job arrivals and machine breakdowns, and it is not known how well GP generalises over both DJSS problem instances with no machine breakdown to problem instances with machine breakdown. Therefore, this paper investigates the generality of GP for DJSS problem with dynamic job arrivals and machine breakdowns. To do this, a machine breakdown specific DJSS dataset is proposed, and an analysis procedure is used to observe the differences in the structures of the GP rules when evolved under different machine breakdown scenarios. The results show that performance and the distributions of the terminals for the evolved rules is sensitive to the frequency of machine breakdowns in the training instances used to evolve the rules.