A PSO-based hyper-heuristic for evolving dispatching rules in job shop scheduling

Abstract

Automated heuristic design for job shop scheduling has been an interesting and challenging research topic in the last decade. Various machine learning and optimising techniques, usually referred to as hyper-heuristics, have been applied to facilitate the design task. Two main approaches are either to utilise a general structure for dispatching rules and optimise its parameters or to simultaneously search for suitable structures and their parameters. Each approach has its own advantages and disadvantages. In this paper, we focus on the first approach and develop new representations that are flexible enough to represent diverse rules and powerful enough to cope with complex shop conditions. Particle swarm optimisation is used in the proposed hyper-heuristic to find optimal rules based on the representations. The results suggest that the new representations are effective for different shop conditions and obtained rules are very competitive as compared to those evolved by genetic programming. Analyses also show that the proposed hyper-heuristic is significantly faster than genetic programming based hyper-heuristic.