A PSO-Based Reference Point Adaption Method for Genetic Programming Hyper-Heuristic in Many-Objective Job Shop Scheduling

Job Shop Scheduling is an important combinatorial optimisation problem in practice. It usually contains many (four or more) potentially conflicting objectives such as makespan and mean weighted tardiness. On the other hand, evolving dispatching rules using genetic programming has demonstrated to be a promising approach to solving job shop scheduling due to its flexibility and scalability. In this paper, we aim to solve many-objective job shop scheduling with genetic programming and NSGA-III. However, NSGA-III is originally designed to work with uniformly distributed reference points which do not match well with the discrete and non-uniform Pareto front in job shop scheduling problems, resulting in many useless points during evolution. These useless points can significantly affect the performance of NSGA-III and genetic programming. To address this issue and inspired by particle swarm optimisation, a new reference point adaptation mechanism has been proposed in this paper. Experiment results on many-objective benchmark job shop scheduling instances clearly show that prominent improvement in performance can be achieved upon using our reference point adaptation mechanism in NSGA-III and genetic programming.