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Flexible Workshop Scheduling Decision Based on Heuristic Algorithm

Received: 11 December 2018    Accepted:     Published: 12 December 2018
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Abstract

In view of the realistic scenes in the flexible shop scheduling problem, the models are abstracted from different machining processes and machine tool failures. For this NP-hard problem, consider a variety of flexible scheduling heuristics, compare their global search and local search performance and discuss the adaptability of different scenarios. Scenario 1 uses a tabu search algorithm and defines the scope of each decision based on analysis and practice.;Scenario 2 analyzes the problems of CNC tool change, loading and unloading matching, process information preservation, etc. The algorithm selection is based on the comparative discussion of model one, and innovatively applies the tabu search algorithm idea to the recombination and mutation part of the genetic algorithm. The model can better encode the process information while ensuring strong local search ability, and adjust the search range of the model to solve the planning time convergence problem, and adjust the order to solve the "circular decision" problem in the model; Scenario 3 adds CNC random fault simulation, re-plans the decision model call time, and redesigns the process save decision of model two. In the model promotion, the algorithm of multi-RGV scheduling problem is discussed, and the applicability and efficiency of the model are clarified.

DOI 10.11648/j.sd.20180606.33
Published in Science Discovery (Volume 6, Issue 6, December 2018)
Page(s) 521-528
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Flexible Shop Scheduling, Tabu Search Algorithm, Genetic Algorithm

References
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[2] 王雷,蔡劲草.基于可变重调度区间的柔性作业车间动态调度策略[J].南京航空航天大学学报,2018,(3):397-403. DOI:10.16356/j.1005-2615.2018.03.016。
[3] 黄学文,张晓彤,艾亚晴.基于蚁群算法的多加工路线柔性车间调度问题[J].计算机集成制造系统,2018,(3):558-569. DOI:10.13196/j.cims.2018.03.003。
[4] 屈迟文,傅彦铭,罗明山, 等.求解柔性作业车间调度问题的鸟群算法[J].计算机工程与应用,2018,(17):249-257. DOI:10.3778/j.issn.1002-8331.1705-0282。
[5] 吕聪,魏康林.柔性车间调度问题的协作混合帝国算法[J].计算机应用,2018,(7):1882-1887. DOI:10.11772/j.issn.1001-9081.2017122933。
[6] 杨煜俊,陈业.求解柔性机器人车间调度问题的混合蚁群算法[J].计算机工程与应用,2018,(13):160-167. DOI:10.3778/j.issn.1002-8331.1702-0187。
[7] 吴焱明,刘永强,张栋, 等.基于遗传算法的RGV动态调度研究[J].起重运输机械,2012,(6):20-23. DOI:10.3969/j.issn.1001-0785.2012.06.006。
[8] 段国林,查建中,徐安平, 等.启发式算法及其在工程中的应用[J].机械设计,2000,(6):1-5. DOI:10.3969/j.issn.1001-2354.2000.06.001。
[9] 许凯波,鲁海燕,程毕芸, 等.求解TSP的改进信息素二次更新与局部优化蚁群算法[J].计算机应用,2017,(6):1686-1691. DOI:10.11772/j.issn.1001-9081.2017.06.1686。
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Author Information
  • Institute of Information and Electronics, Beijing Institute of Technology, Beijing, China

  • Institute of Information and Electronics, Beijing Institute of Technology, Beijing, China

  • Institute of Information and Electronics, Beijing Institute of Technology, Beijing, China

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  • APA Style

    Xuanzheng Wang, Haoyang Luo, Juntang Zhang. (2018). Flexible Workshop Scheduling Decision Based on Heuristic Algorithm. Science Discovery, 6(6), 521-528. https://doi.org/10.11648/j.sd.20180606.33

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    ACS Style

    Xuanzheng Wang; Haoyang Luo; Juntang Zhang. Flexible Workshop Scheduling Decision Based on Heuristic Algorithm. Sci. Discov. 2018, 6(6), 521-528. doi: 10.11648/j.sd.20180606.33

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    AMA Style

    Xuanzheng Wang, Haoyang Luo, Juntang Zhang. Flexible Workshop Scheduling Decision Based on Heuristic Algorithm. Sci Discov. 2018;6(6):521-528. doi: 10.11648/j.sd.20180606.33

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  • @article{10.11648/j.sd.20180606.33,
      author = {Xuanzheng Wang and Haoyang Luo and Juntang Zhang},
      title = {Flexible Workshop Scheduling Decision Based on Heuristic Algorithm},
      journal = {Science Discovery},
      volume = {6},
      number = {6},
      pages = {521-528},
      doi = {10.11648/j.sd.20180606.33},
      url = {https://doi.org/10.11648/j.sd.20180606.33},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.sd.20180606.33},
      abstract = {In view of the realistic scenes in the flexible shop scheduling problem, the models are abstracted from different machining processes and machine tool failures. For this NP-hard problem, consider a variety of flexible scheduling heuristics, compare their global search and local search performance and discuss the adaptability of different scenarios. Scenario 1 uses a tabu search algorithm and defines the scope of each decision based on analysis and practice.;Scenario 2 analyzes the problems of CNC tool change, loading and unloading matching, process information preservation, etc. The algorithm selection is based on the comparative discussion of model one, and innovatively applies the tabu search algorithm idea to the recombination and mutation part of the genetic algorithm. The model can better encode the process information while ensuring strong local search ability, and adjust the search range of the model to solve the planning time convergence problem, and adjust the order to solve the "circular decision" problem in the model; Scenario 3 adds CNC random fault simulation, re-plans the decision model call time, and redesigns the process save decision of model two. In the model promotion, the algorithm of multi-RGV scheduling problem is discussed, and the applicability and efficiency of the model are clarified.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Flexible Workshop Scheduling Decision Based on Heuristic Algorithm
    AU  - Xuanzheng Wang
    AU  - Haoyang Luo
    AU  - Juntang Zhang
    Y1  - 2018/12/12
    PY  - 2018
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    DO  - 10.11648/j.sd.20180606.33
    T2  - Science Discovery
    JF  - Science Discovery
    JO  - Science Discovery
    SP  - 521
    EP  - 528
    PB  - Science Publishing Group
    SN  - 2331-0650
    UR  - https://doi.org/10.11648/j.sd.20180606.33
    AB  - In view of the realistic scenes in the flexible shop scheduling problem, the models are abstracted from different machining processes and machine tool failures. For this NP-hard problem, consider a variety of flexible scheduling heuristics, compare their global search and local search performance and discuss the adaptability of different scenarios. Scenario 1 uses a tabu search algorithm and defines the scope of each decision based on analysis and practice.;Scenario 2 analyzes the problems of CNC tool change, loading and unloading matching, process information preservation, etc. The algorithm selection is based on the comparative discussion of model one, and innovatively applies the tabu search algorithm idea to the recombination and mutation part of the genetic algorithm. The model can better encode the process information while ensuring strong local search ability, and adjust the search range of the model to solve the planning time convergence problem, and adjust the order to solve the "circular decision" problem in the model; Scenario 3 adds CNC random fault simulation, re-plans the decision model call time, and redesigns the process save decision of model two. In the model promotion, the algorithm of multi-RGV scheduling problem is discussed, and the applicability and efficiency of the model are clarified.
    VL  - 6
    IS  - 6
    ER  - 

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