Evolving machine-specific dispatching rules for a two-machine job shop using genetic programming

Rachel Hunt, Mark Johnston, Mengjie Zhang

Abstract: Job Shop Scheduling (JSS) involves determining a schedule for processing jobs on machines to optimise some measure of delivery speed or customer satisfaction. We investigate a genetic programming based hyper-heuristic (GPHH) approach to evolving dispatching rules for a two-machine job shop in both static and dynamic environments. In the static case the proposed GPHH method can represent and discover optimal dispatching rules. In the dynamic case we investigate two representations (using a single rule at both machines and evolving a specialised rule for each machine) and the effect of changing the training problem instances throughout evolution. Results show that relative performance of these methods is dependent on the testing instances.