A genetic algorithm for flowshop sequencing
Computers and Operations Research - Special issue on genetic algorithms
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
An Efficient Genetic Algorithm for Job Shop Scheduling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
Investigating Parallel Genetic Algorithms on Job Shop Scheduling Problems
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
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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 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 and audio parts manufacturer. CCX achieved remarkable results on the actual job shop scheduling problem.