Job shop scheduling with genetic algorithm-based hyperheuristic approach

Abstract

Job shop scheduling problems are NP-hard problems that have been studied extensively in the literature as well as in real-life. Many factories all over the world produce worth millions of dollars with job shop type production systems. It is crucial to use effective production scheduling methods to reduce costs and increase productivity. Hyperheuristics are fast-implementing, low-cost, and powerful enough to deal with different problems effectively since they need limited problem-specific information. In this paper, a genetic algorithm-based hyperheuristic (GAHH) approach is proposed for job shop scheduling problems. Twenty-six dispatching rules are used as low-level heuristics. We use a set of benchmark problems from OR-Library to test the proposed algorithm. The performance of the proposed approach is compared with genetic algorithm, simulating annealing, particle swarm optimization and some of dispatching rules. Computational experiments show that the proposed genetic algorithm-based hyperheuristic approach finds optimal results or produces better solutions than compared methods.