Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm |
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| Authors: | James C Chen Cheng-Chun Wu Chia-Wen Chen Kou-Huang Chen |
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| Affiliation: | 1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan, ROC;2. Data System Consulting Co., Taipei, Taiwan, ROC;3. Department of Industrial and Information Management, National Cheng Kung University, Tainan, Taiwan, ROC;4. Department of Industrial Engineering and Management, China University of Science and Technology, Taipei, Taiwan, ROC;1. Bilgesu AK, Ondokuz May?s University Industrial Engineering Department, Samsun-55139, Turkey;2. Erdem KOÇ, Ondokuz May?s University Mechanical Engineering Department, Samsun-55139, Turkey;1. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798 Singapore, Singapore;2. Department of Industrial Engineering, Yasar University, Bomova, Izmir, Turkey;3. School of Computer, Liaocheng University, Liaocheng 252059, PR China;4. Department of Information Engineering, Binzhou University, Binzhou 256603, PR China;1. Department of Industrial Management, Vali-e-Asr University, Rafsanjan, Iran;2. Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran;1. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;2. Computer School, Liaocheng University, Liaocheng 252000, PR China;3. Singapore Institute of Manufacturing Technology, Nanyang Drive 638075, Singapore |
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| Abstract: | Based on Genetic Algorithm (GA) and Grouping Genetic Algorithm (GGA), this research develops a scheduling algorithm for job shop scheduling problem with parallel machines and reentrant process. This algorithm consists of two major modules: machine selection module (MSM) and operation scheduling module (OSM). MSM helps an operation to select one of the parallel machines to process it. OSM is then used to arrange the sequences of all operations assigned to each machine. A real weapon production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of late delivery in military orders and high cost of equipment investment, total tardiness, total machine idle time and makespan are important performance measures used in this study. Based on the design of experiments, the parameters setting for GA and GGA are identified. Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice. |
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