A new genetic representation and common cluster crossover for job shop scheduling problems

Abstract

This paper describes a genetic algorithm approach for sequencing problems especially for job shop scheduling. In actual problems, setup time should be optimized. In order to reduce setup time, we developed a new genetic representation and an efficient crossover operator called Common Cluster Crossover (CCX). In our representation, chromosomes represent the shift of the order in a sequence. To preserve sub-sequences in crossover operations, we implemented the process to identify the cluster of the sub-sequences and applied CCX that exchanges common clusters between two parents. The approach was tested on two standard benchmarks and applied to an audio parts manufacturer. CCX achieved remarkable results on the actual job shop scheduling problem.