Abstract
A Taguchi-based genetic algorithm (TBGA) is proposed as an improved genetic algorithm to solve the job-shop scheduling problems (JSP). The TBGA combines the powerful global exploration capabilities of conventional genetic algorithm (GA) with the Taguchi method that exploits optimal offspring. The latter method is used as a new crossover and is incorporated in the crossover operation of a GA. The reasoning ability of the Taguchi-based crossover can systematically select the better genes to achieve crossover and, consequently, enhance the GA. Furthermore, mutation is designed to have the neighbor search technique of performing the fine-tuning on the positions of jobs for the JSP. Therefore, the proposed TBGA approach possesses the merits of global exploration and robustness. The proposed TBGA approach is effectively applied to solve the famous Fisher-Thompson and Lawrence benchmarks of the JSP. In these studied problems, there are numerous local optima so that these studied problems are challenging enough for evaluating the performances of any proposed evolutionary approaches. The computational experiments show that the proposed TBGA approach can obtain both better and more robust results than those evolutionary methods reported recently.
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References
Cheng R, Gen M, Tsujimura Y (1996) A tutorial survey of job-shop scheduling problems using genetic algorithms: Part i representation. Comput Ind Eng 30:983–997
Gen M, Cheng R (1997) Genetic algorithms and engineering design. John Wiley and Sons, New York
Gen M, Tsujimura Y, Kubota E (1994) Solving job-shop scheduling problems by genetic algorithm, Proc. of the IEEE International Conference on Systems, Man and Cybernetics, Texas, pp. 1577–1582
Goldberg DE (1989) Genetic algorithms in search, Optimization and machine learning. Addison-Wesley, Boston, MA
Kim GH, Lee CSG (1998) Genetic reinforcement learning approach to the heterogeneous machine scheduling problem. IEEE Trans Robot Autom 14:879–893
Li G, Li JY (2003) On job-shop scheduling by hybrid genetic algorithm. J. of Tianjin University of Science and Technology 36:239–242
Liu, TK, Tsai JT, Chou JH (2006) Improved genetic algorithm for job-shop scheduling problem. Int J Adv Manuf Technol 27(9–10):1021–1029
Montgomery DC (1991) Design and analysis of experiments. Wiley, New York
Ono I, Yamamura M, Kobayashi S (1996) A genetic algorithm for job-shop scheduling problem using job-based order crossover. Proc. of the IEEE International Conference on Evolutionary Computation, Nagoya, pp. 547–552
Park SH (1996) Robust design and analysis for quality engineering. Chapman & Hall, London
Phadke MS (1989) Quality engineering using robust design. Prentice-Hall, Upper Saddle River, NJ
Ross PJ (1989) Taguchi techniques for quality engineering. McGraw-Hill, Singapore
Shi G (1997) A genetic algorithm applied to a classic job-shop scheduling problem. Int J Syst Sci 28:25–32
Taguchi G, Chowdhury S, Taguchi S (2000) Robust engineering. McGraw-Hill, New York
Tsujimura Y, Gen M, Cheng R (1997) Improved genetic algorithms for solving job-shop scheduling problem. Eng Design Auto 3:133–144
Tsujimura Y, Sugimoto T, Mafune Y, Gen M (1999) A genetic algorithm for job-shop scheduling by means of symbiosis mechanism, Proc. of the 3rd Australia-Japan Joint Workshop on Intelligent and Evolutionary Systems, Canberra, pp. 288–291
Tsujimura Y, Mafune Y, Gen M (2001) Effects of symbiotic evolution in genetic algorithms for job-shop scheduling, Proc. of the IEEE 34th International Conference on System Sciences, Hawaii, pp. 1–7
Wang L (2001) Intelligent optimization algorithms with applications. Tsinghua University Press, Beijing
Wang L, Zheng DZ (2001) An effective hybrid optimization strategy for job-shop scheduling problems. Comput Oper Res 28:585–596
Wang L, Zheng DZ (2002) A modified genetic algorithm for job shop scheduling. Int J Adv Manuf Technol 20:72–76
Wu Y (2000) Taguchi methods for robust design. The American Society of Mechanical Engineers, New York
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Tsai, JT., Liu, TK., Ho, WH. et al. An improved genetic algorithm for job-shop scheduling problems using Taguchi-based crossover. Int J Adv Manuf Technol 38, 987–994 (2008). https://doi.org/10.1007/s00170-007-1142-5
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DOI: https://doi.org/10.1007/s00170-007-1142-5