Evolving Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling via Genetic Programming Hyper-heuristics

Abstract

Dynamic flexible job shop scheduling (DFJSS) is one of the well-known combinational optimisation problems, which aims to handle machine assignment (routing) and operation sequencing (sequencing) simultaneously in dynamic environment. Genetic programming, as a hyper-heuristic method, has been successfully applied to evolve the routing and sequencing rules for DFJSS, and achieved promising results. In the actual production process, it is necessary to get a balance between several objectives instead of simply focusing only one objective. No existing study considered solving multi-objective DFJSS using genetic programming. In order to capture multi-objective nature of job shop scheduling and provide different trade-offs between conflicting objectives, in this paper, two well-known multi-objective optimisation frameworks, i.e. non-dominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2), are incorporated into the genetic programming hyper-heuristic method to solve the multi-objective DFJSS problem. Experimental results show that the strategy of NSGA-II incorporated into genetic programming hyper-heuristic performs better than SPEA2-based GPHH, as well as the weighted sum approaches, in the perspective of both training performance and generalisation.