A comparison of two chromosome representation schemes used in solving a family-based scheduling problem

Authors:
Chen-Fu Chen;Muh-Cherng Wu;Yi-Hsun Li;Pang-Hao Tai;Chie-Wun Chiou
Affiliations:
Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan 300, ROC;Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan 300, ROC;Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan 300, ROC;Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan 300, ROC;Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, Taiwan 300, ROC
Venue:
Robotics and Computer-Integrated Manufacturing
Year:
2013

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Abstract

Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new chromosome (solution) representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two chromosome representation schemes (S"o"l"d and S"n"e"w) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X_Y, where X represents an algorithmic mechanism and Y represents a chromosome representation. Experiment results indicate that GA_ S"n"e"w outperforms GA_S"o"l"d, and ACO_S"n"e"w also outperforms ACO_S"o"l"d. These findings reveal the importance of developing new chromosome representations in the application of meta-heuristic algorithms.