A study on flowshop scheduling problem combining Taguchi experimental design and genetic algorithm |
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| Affiliation: | 1. Department of Industrial Engineering & Management, National Yunlin University of Science and Technology, 123, Sec. 3, University Road Touliu, Yunlin 640, Taiwan, ROC;2. Department of Industrial Management, National Formosa University, 64 Wun-Hwa Road Huwei, Yunlin 632, Taiwan, ROC;1. Department of Computer Engineering, Wroc?aw University of Technology, Janiszewskiego 11-17, 50-372 Wroc?aw, Poland;2. Department of Control Systems and Mechatronics, Wroc?aw University of Technology, Janiszewskiego 11-17, 50-372 Wroc?aw, Poland;1. College of Computer and Information Science, Fujian Agriculture and Forestry University, Fuzhou, Fujian Province 350002, PR China;2. Key Laboratory of Smart Agriculture and Forestry (Fujian Agriculture and Forestry University), Fujian Province University, Fuzhou, Fujian Province 350002, PR China;3. J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA |
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| Abstract: | As genetic algorithm parameters vary depending on different problem types when applying genetic algorithm to reach global optimum, appropriate design value selection has significant impact on the efficiency of genetic algorithm. However, most users adjust parameters manually based on the reference values of previous literature. Such trial-and-error method is time-consuming, ineffective, and often it could not locate the optimal combination. Therefore, in flowshop scheduling problems, this research anticipates to complete optimal parameter combination design in genetic algorithm using Taguchi experimental design. According to the research results, different ways of producing initial solution have significant influence on this research topic. Consequently, confirmation experiment is conducted using the optimal parameter combination obtained from the research results. It is found that the predicted value of signal-to-noise ratio (S/N ratio) and its actual value exists deviation of 0.238%, indicating repetitiveness and robustness of the obtained parameter combination. Hence, this research method can effectively reduce time spent on parameter design using genetic algorithm and increase efficiency of algorithm. |
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