Using local search to evaluate dispatching rules in dynamic job shop scheduling

Abstract

Improving scheduling methods in manufacturing environments such as job shops offers the potential to increase throughput, decrease costs, and therefore increase profit. This makes scheduling an important aspect in the manufacturing industry. Job shop scheduling has been widely studied in the academic literature because of its realworld applicability and difficult nature. Dispatching rules are the most common means of scheduling in dynamic environments. We use genetic programming to search the space of potential dispatching rules. Dispatching rules are often short-sighted as they make one instantaneous decision at each decision point. We incorporate local search into the evaluation of dispatching rules to assess the quality of decisions made by dispatching rules and encourage the dispatching rules to make good local decisions for effective overall performance. Results show that the inclusion of local search in evaluation led to the evolution of dispatching rules which make better decisions over the local time horizon, and attain lower total weighted tardiness. The advantages of using local search as a tie-breaking mechanism are not so pronounced.