A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems

This paper describes a memory enhanced evolutionary algorithm (EA) approach to the dynamic job shop scheduling problem. Memory enhanced EAs have been widely investigated for other dynamic optimization problems with changing fitness landscapes, but only when associated with a fixed search space. In dynamic scheduling, the search space shifts as jobs are completed and new jobs arrive, so memory entries that describe specific points in the search space will become infeasible over time. The relative importances of jobs in the schedule also change over time, so previously good points become increasingly irrelevant. We describe a classifier-based memory for abstracting and storing information about schedules that can be used to build similar schedules at future times. We compared the memory enhanced EA with a standard EA and several common EA diversity techniques both with and without memory. The memory enhanced EA improved performance over the standard EA, while diversity techniques decreased performance.