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A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm

Author

Listed:
  • Lili Dai

    (Institute of Smart Materials and Applied Technology, Lianyungang Normal College, Lianyungang 222006, China)

  • He Lu

    (Institute of Smart Materials and Applied Technology, Lianyungang Normal College, Lianyungang 222006, China
    School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China)

  • Dezheng Hua

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China)

  • Xinhua Liu

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China)

  • Hongming Chen

    (School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 211006, China)

  • Adam Glowacz

    (Department of Automatic, Control and Robotics, AGH University of Science and Technology, 30-059 Kraków, Poland)

  • Grzegorz Królczyk

    (Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland)

  • Zhixiong Li

    (Faculty of Mechanical Engineering, Opole University of Technology, 45-758 Opole, Poland
    Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea)

Abstract

Due to the complexity of the production shop in discrete manufacturing industry, the traditional genetic algorithm (GA) cannot solve the production scheduling problem well. In order to enhance the GA-based method to solve the production scheduling problem effectively, the simulated annealing algorithm (SAA) is used to develop an improved hybrid genetic algorithm. Firstly, the crossover probability and mutation probability of the genetic operation are adjusted, and the elite replacement operation is adopted for simulated annealing operator. Then, a mutation method is used for the comparison and replacement of the genetic operations to obtain the optimal value of the current state. Lastly, the proposed hybrid genetic algorithm is compared with several scheduling algorithms, and the superiority and efficiency of the proposed method are verified in solving the production scheduling.

Suggested Citation

  • Lili Dai & He Lu & Dezheng Hua & Xinhua Liu & Hongming Chen & Adam Glowacz & Grzegorz Królczyk & Zhixiong Li, 2022. "A Novel Production Scheduling Approach Based on Improved Hybrid Genetic Algorithm," Sustainability, MDPI, vol. 14(18), pages 1-15, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11747-:d:918934
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    References listed on IDEAS

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    1. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    2. Bellabdaoui, A. & Teghem, J., 2006. "A mixed-integer linear programming model for the continuous casting planning," International Journal of Production Economics, Elsevier, vol. 104(2), pages 260-270, December.
    3. Zhang, Rui & Chang, Pei-Chann & Wu, Cheng, 2013. "A hybrid genetic algorithm for the job shop scheduling problem with practical considerations for manufacturing costs: Investigations motivated by vehicle production," International Journal of Production Economics, Elsevier, vol. 145(1), pages 38-52.
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    Cited by:

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    2. Taotao Xu & Lijian Yao & Lijun Xu & Qinhan Chen & Zidong Yang, 2023. "Image Segmentation of Cucumber Seedlings Based on Genetic Algorithm," Sustainability, MDPI, vol. 15(4), pages 1-15, February.

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