Genetic algorithm with fuzzy logic controller for preemptive and non-preemptive job-shop scheduling problems |
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| Affiliation: | 1. Virus Research Group, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea;2. Drug Information Platform Center, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea;3. College of Pharmacy & Graduate School of Pharmaceutical Sciences, Ewha Womens University, Seoul 03760, Republic of Korea;4. Green Carbon Catalysis Group, Carbon Resources Institute, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea;5. Department of Medicinal Chemistry and Pharmacology, University of Science and Technology, Daejeon 34114, Republic of Korea;6. Center for Convergent Research of Emerging Virus Infection, Korea Research Institute of Chemical Technology, Daejeon 34114, Republic of Korea;1. 108 MaxwelL H. Gluck Equine Research Center, Department of Veterinary Science, University of Kentucky, Lexington, KY;2. Department of Veterinary Science, University of Kentucky Veterinary Diagnostic Laboratory, University of Kentucky, Lexington, KY;1. Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, China;2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, China;1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;2. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China |
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| Abstract: | In this paper, we propose a new genetic algorithm (GA) with fuzzy logic controller (FLC) for dealing with preemptive job-shop scheduling problems (p-JSP) and non-preemptive job-shop scheduling problems (np-JSP). The proposed algorithm considers the preemptive cases of activities among jobs under single machine scheduling problems. For these preemptive cases, we first use constraint programming and secondly develop a new gene representation method, a new crossover and mutation operators in the proposed algorithm.However, the proposed algorithm, as conventional GA, also has a weakness that takes so much time for the fine-tuning of genetic parameters. FLC can be used for regulating these parameters.In this paper, FLC is used to adaptively regulate the crossover ratio and the mutation ratio of the proposed algorithm. To prove the performance of the proposed FLC, we divide the proposed algorithm into two cases: the GA with the FLC (pro-fGA) and the GA without the FLC (pro-GA).In numerical examples, we apply the proposed algorithms to several job-shop scheduling problems and the results applied are analyzed and compared. Various experiments show that the results of pro-fGA outperform those of pro-GA. |
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