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Mathematical modeling and a hybrid evolutionary algorithm for process planning

Author

Listed:
  • Qihao Liu

    (Huazhong University of Science and Technology)

  • Xinyu Li

    (Huazhong University of Science and Technology)

  • Liang Gao

    (Huazhong University of Science and Technology)

Abstract

Process planning is an essential part of the manufacturing system linking the designing and practical manufacturing. However, the reported process planning models are too simple to describe all characteristics because of the complexity of process planning. Therefore, a new mixed-integer linear programming (MILP) mathematical model is established based on OR-node of the network graph. In the model, the linear expression of the OR-node controlling function as well as three types of changing costs are first established. Beside, considering the OR-node selection state in the encoding and decoding method, a hybrid evolutionary algorithm (HEA) is designed to combine a genetic algorithm with a simulated annealing algorithm. The tournament selection method is adopted in the proposed HEA, and the discussion on the tournament size is conducted on the open problems to make the algorithm designing more reasonable and scientific. The HEA and the new MILP model are both tested on series of numerical experiments which are carried on the existing benchmarks as well as some randomly generated cases. The behavior of both two methods can verify their effectiveness and superiority successfully.

Suggested Citation

  • Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:3:d:10.1007_s10845-020-01703-w
    DOI: 10.1007/s10845-020-01703-w
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    References listed on IDEAS

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    1. Hyun Cheol Lee & Chunghun Ha, 2019. "Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    2. Yingxin Ye & Tianliang Hu & Yan Yang & Wendan Zhu & Chengrui Zhang, 2020. "A knowledge based intelligent process planning method for controller of computer numerical control machine tools," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1751-1767, October.
    3. Faycal A. Touzout & Lyes Benyoucef, 2019. "Multi-objective sustainable process plan generation in a reconfigurable manufacturing environment: exact and adapted evolutionary approaches," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2531-2547, April.
    4. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    5. Wei Wang & Yingguang Li & Lingling Huang, 2018. "Rule and branch-and-bound algorithm based sequencing of machining features for process planning of complex parts," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1329-1336, August.
    6. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    7. Abdullah Falih & Ahmed Z. M. Shammari, 2020. "Hybrid constrained permutation algorithm and genetic algorithm for process planning problem," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1079-1099, June.
    8. Guiliang Gong & Qianwang Deng & Raymond Chiong & Xuran Gong & Hezhiyuan Huang & Wenwu Han, 2020. "Remanufacturing-oriented process planning and scheduling: mathematical modelling and evolutionary optimisation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(12), pages 3781-3799, June.
    9. Christodoulos Floudas & Xiaoxia Lin, 2005. "Mixed Integer Linear Programming in Process Scheduling: Modeling, Algorithms, and Applications," Annals of Operations Research, Springer, vol. 139(1), pages 131-162, October.
    10. Zhigang Jiang & Ya Jiang & Yan Wang & Hua Zhang & Huajun Cao & Guangdong Tian, 2019. "A hybrid approach of rough set and case-based reasoning to remanufacturing process planning," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 19-32, January.
    11. Oleh Sobeyko & Lars Mönch, 2017. "Integrated process planning and scheduling for large-scale flexible job shops using metaheuristics," International Journal of Production Research, Taylor & Francis Journals, vol. 55(2), pages 392-409, January.
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    Cited by:

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    2. Luo, Kaiping & Shen, Guangya & Li, Liheng & Sun, Jianfei, 2023. "0-1 mathematical programming models for flexible process planning," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1160-1175.

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