IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i9p1433-d911458.html
   My bibliography  Save this article

Harvester Maintenance Resource Scheduling Optimization, Based on the Combine Harvester Operation and Maintenance Platform

Author

Listed:
  • Weipeng Zhang

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Bo Zhao

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Liming Zhou

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Jizhong Wang

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Conghui Qiu

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Kang Niu

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

  • Fengzhu Wang

    (State Key Laboratory of Soil Plant Machine System Technology, China Academy of Agricultural Mechanization Science Group Co., Ltd., Beijing 100083, China)

Abstract

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations.

Suggested Citation

  • Weipeng Zhang & Bo Zhao & Liming Zhou & Jizhong Wang & Conghui Qiu & Kang Niu & Fengzhu Wang, 2022. "Harvester Maintenance Resource Scheduling Optimization, Based on the Combine Harvester Operation and Maintenance Platform," Agriculture, MDPI, vol. 12(9), pages 1-22, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1433-:d:911458
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/9/1433/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/9/1433/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaabane, K. & Khatab, A. & Diallo, C. & Aghezzaf, E.-H. & Venkatadri, U., 2020. "Integrated imperfect multimission selective maintenance and repairpersons assignment problem," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Junwei & An, Youjun & Dong, Yuanfa & Hu, Jiawen & Li, Yinghe & Zhao, Ziye, 2023. "Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Zhou, Kai-Li & Cheng, De-Jun & Zhang, Han-Bing & Hu, Zhong-tai & Zhang, Chun-Yan, 2023. "Deep learning-based intelligent multilevel predictive maintenance framework considering comprehensive cost," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Ait Mokhtar, El Hassene & Laggoune, Radouane & Chateauneuf, Alaa, 2023. "Imperfect maintenance modeling and assessment of repairable multi-component systems," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    4. Hamzea Al-Jabouri & Ahmed Saif & Claver Diallo, 2023. "Robust selective maintenance optimization of series–parallel mission-critical systems subject to maintenance quality uncertainty," Computational Management Science, Springer, vol. 20(1), pages 1-31, December.
    5. Zhang, Qin & Fang, Zhigeng & Cai, Jiajia, 2021. "Preventive replacement policies with multiple missions and maintenance triggering approaches," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Liu, Lujie & Yang, Jun & Kong, Xuefeng & Xiao, Yiyong, 2022. "Multi-mission selective maintenance and repairpersons assignment problem with stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    7. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2022. "A two-stage stochastic programming model for selective maintenance optimization," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    8. Ghorbani, Milad & Nourelfath, Mustapha & Gendreau, Michel, 2024. "Stochastic programming for selective maintenance optimization with uncertainty in the next mission conditions," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    9. Zhang, Qin & Liu, Yu & Xiahou, Tangfan & Huang, Hong-Zhong, 2023. "A heuristic maintenance scheduling framework for a military aircraft fleet under limited maintenance capacities," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    10. uit het Broek, Michiel A.J. & Teunter, Ruud H. & de Jonge, Bram & Veldman, Jasper, 2021. "Joint condition-based maintenance and condition-based production optimization," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    11. Zuo, Fei & Zio, Enrico & Xu, Yue, 2023. "Bi-objective optimization of the scheduling of risk-related resources for risk response," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    12. A. Khatab & C. Diallo & E.-H. Aghezzaf & U. Venkatadri, 2022. "Optimization of the integrated fleet-level imperfect selective maintenance and repairpersons assignment problem," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 703-718, March.
    13. Chen, Zhaoxiang & Chen, Zhen & Zhou, Di & Pan, Ershun, 2023. "Energy-oriented opportunistic maintenance optimization of continuous process manufacturing systems with two types of stochastic durations," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    14. Ma, Weining & Zhang, Qin & Xiahou, Tangfan & Liu, Yu & Jia, Xisheng, 2023. "Integrated selective maintenance and task assignment optimization for multi-state systems executing multiple missions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    15. Yin, Mingang & Liu, Yu & Liu, Shuntao & Chen, Yiming & Yan, Yutao, 2023. "Scheduling heterogeneous repair channels in selective maintenance of multi-state systems with maintenance duration uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1433-:d:911458. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.