Flexible Workshop Scheduling Decision Based on Heuristic Algorithm

Xuanzheng Wang; Haoyang Luo; Juntang Zhang

doi:doi:10.11648/j.sd.20180606.33

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Flexible Workshop Scheduling Decision Based on Heuristic Algorithm

Xuanzheng Wang, Haoyang Luo, Juntang Zhang

Published in Science Discovery (Volume 6, Issue 6)

Received: 11 December 2018 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.

Published in	Science Discovery (Volume 6, Issue 6)
DOI	10.11648/j.sd.20180606.33
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), 2018. Published by Science Publishing Group

Keywords

Flexible Shop Scheduling, Tabu Search Algorithm, Genetic Algorithm

References

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[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。
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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://article.sciencepublishinggroup.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
N1  - https://doi.org/10.11648/j.sd.20180606.33
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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Author Information

Xuanzheng Wang

Institute of Information and Electronics, Beijing Institute of Technology, Beijing, China
Haoyang Luo

Institute of Information and Electronics, Beijing Institute of Technology, Beijing, China
Juntang Zhang

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

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Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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://article.sciencepublishinggroup.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}
}

Copy | Download

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
N1  - https://doi.org/10.11648/j.sd.20180606.33
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  -

Copy | Download