IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i21p3933-d950914.html
   My bibliography  Save this article

Dynamic Scheduling Strategy for Shared Agricultural Machinery for On-Demand Farming Services

Author

Listed:
  • Li Ma

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Minghan Xin

    (College of Engineering, Northeast Agricultural University, Harbin 150030, China)

  • Yi-Jia Wang

    (Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong 999077, China)

  • Yanjiao Zhang

    (Baoneng Automobile Group, Shenzhen 518000, China)

Abstract

With the development of the “Internet +” model and the sharing economy model, the “online car-hailing” operation model has promoted the emergence of “online-hailing agricultural machinery”. This new supply and demand model of agricultural machinery has brought greater convenience to the marketization of agricultural machinery services. However, although this approach has solved the use of some agricultural machinery resources, it has not yet formed a scientific and systematic scheduling model. Referring to the existing agricultural machinery scheduling modes and the actual demand of agricultural production, based on the idea of resource sharing, in this research, the soft and hard time windows were combined to carry out the research on the dynamic demand scheduling strategy of agricultural machinery. The main conclusions obtained include: (1) Based on the ideas of order resource sharing and agricultural machinery resource sharing, a general model of agricultural machinery scheduling that meet the dynamic needs was established, and a more scientific scheduling plan was proposed; (2) Based on the multi-population coevolutionary genetic algorithm, the dynamic scheduling scheme for shared agricultural machinery for on-demand farming services was obtained, which can reasonably insert the dynamic orders on the basis of the initial scheduling scheme, and realize the timely response to farmers’ operation demands; (3) By comparing with the actual production situation, the path cost and total operating cost were saved, thus the feasibility and effectiveness of the scheduling model were clarified.

Suggested Citation

  • Li Ma & Minghan Xin & Yi-Jia Wang & Yanjiao Zhang, 2022. "Dynamic Scheduling Strategy for Shared Agricultural Machinery for On-Demand Farming Services," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:3933-:d:950914
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/21/3933/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/21/3933/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Schyns, M., 2015. "An ant colony system for responsive dynamic vehicle routing," European Journal of Operational Research, Elsevier, vol. 245(3), pages 704-718.
    2. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan & Qu, Xiaobo, 2022. "Dynamic stochastic electric vehicle routing with safe reinforcement learning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    3. Sadati, Mir Ehsan Hesam & Çatay, Bülent, 2021. "A hybrid variable neighborhood search approach for the multi-depot green vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    4. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2006. "Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 40(2), pages 211-225, May.
    5. Zsuzsanna Nagy & Ágnes Werner-Stark & Tibor Dulai, 2022. "An Artificial Bee Colony Algorithm for Static and Dynamic Capacitated Arc Routing Problems," Mathematics, MDPI, vol. 10(13), pages 1-38, June.
    6. Ferrer, Juan-Carlos & Mac Cawley, Alejandro & Maturana, Sergio & Toloza, Sergio & Vera, Jorge, 2008. "An optimization approach for scheduling wine grape harvest operations," International Journal of Production Economics, Elsevier, vol. 112(2), pages 985-999, April.
    7. Li Ma & Yidi Wang & Meiqiong Ma & Jiyun Bai, 2021. "Research on Multi-Cooperative Combine-Integrated Scheduling Based on Improved NSGA-II Algorithm," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 12(4), pages 1-21, October.
    8. Wadi Khalid Anuar & Lai Soon Lee & Hsin-Vonn Seow & Stefan Pickl, 2022. "A Multi-Depot Dynamic Vehicle Routing Problem with Stochastic Road Capacity: An MDP Model and Dynamic Policy for Post-Decision State Rollout Algorithm in Reinforcement Learning," Mathematics, MDPI, vol. 10(15), pages 1-70, July.
    9. R. Montemanni & L. M. Gambardella & A. E. Rizzoli & A. V. Donati, 2005. "Ant Colony System for a Dynamic Vehicle Routing Problem," Journal of Combinatorial Optimization, Springer, vol. 10(4), pages 327-343, December.
    10. Karin Larsén, 2010. "Effects of machinery‐sharing arrangements on farm efficiency: evidence from Sweden," Agricultural Economics, International Association of Agricultural Economists, vol. 41(5), pages 497-506, September.
    11. Jie Zhang & Yifan Zhu & Tao Wang & Weiping Wang & Rui Wang & Xiaobo Li, 2022. "An Improved Intelligent Auction Mechanism for Emergency Material Delivery," Mathematics, MDPI, vol. 10(13), pages 1-30, June.
    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. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    2. Nikola Mardešić & Tomislav Erdelić & Tonči Carić & Marko Đurasević, 2023. "Review of Stochastic Dynamic Vehicle Routing in the Evolving Urban Logistics Environment," Mathematics, MDPI, vol. 12(1), pages 1-44, December.
    3. Lars M. Hvattum & Arne Løkketangen & Gilbert Laporte, 2006. "Solving a Dynamic and Stochastic Vehicle Routing Problem with a Sample Scenario Hedging Heuristic," Transportation Science, INFORMS, vol. 40(4), pages 421-438, November.
    4. Cristián E. Cortés & Doris Sáez & Alfredo Núñez & Diego Muñoz-Carpintero, 2009. "Hybrid Adaptive Predictive Control for a Dynamic Pickup and Delivery Problem," Transportation Science, INFORMS, vol. 43(1), pages 27-42, February.
    5. Garside, Annisa Kesy & Ahmad, Robiah & Muhtazaruddin, Mohd Nabil Bin, 2024. "A recent review of solution approaches for green vehicle routing problem and its variants," Operations Research Perspectives, Elsevier, vol. 12(C).
    6. Wang, Jianxin & Lim, Ming K. & Liu, Weihua, 2024. "Promoting intelligent IoT-driven logistics through integrating dynamic demand and sustainable logistics operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    7. Yong Wang & Jiayi Zhe & Xiuwen Wang & Yaoyao Sun & Haizhong Wang, 2022. "Collaborative Multidepot Vehicle Routing Problem with Dynamic Customer Demands and Time Windows," Sustainability, MDPI, vol. 14(11), pages 1-37, May.
    8. Alonso Tabares, Diego & Mora-Camino, Felix & Drouin, Antoine, 2021. "A multi-time scale management structure for airport ground handling automation," Journal of Air Transport Management, Elsevier, vol. 90(C).
    9. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    10. Ozbaygin, Gizem & Savelsbergh, Martin, 2019. "An iterative re-optimization framework for the dynamic vehicle routing problem with roaming delivery locations," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 207-235.
    11. Seyfi, Majid & Alinaghian, Mahdi & Ghorbani, Erfan & Çatay, Bülent & Saeid Sabbagh, Mohammad, 2022. "Multi-mode hybrid electric vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 166(C).
    12. Meryem Abid & Mohamed Tabaa & Hanaa Hachimi, 2024. "Electric Vehicle Routing Problem with an Enhanced Vehicle Dispatching Approach Considering Real-Life Data," Energies, MDPI, vol. 17(7), pages 1-27, March.
    13. Tuğçe Taşkıner & Bilge Bilgen, 2021. "Optimization Models for Harvest and Production Planning in Agri-Food Supply Chain: A Systematic Review," Logistics, MDPI, vol. 5(3), pages 1-27, August.
    14. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    15. LIAN, Ying & LUCAS, Flavien & SÖRENSEN, Kenneth, 2022. "On-demand bus routing problem with dynamic stochastic requests and prepositioning," Working Papers 2022004, University of Antwerp, Faculty of Business and Economics.
    16. Novoa, Clara & Storer, Robert, 2009. "An approximate dynamic programming approach for the vehicle routing problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 196(2), pages 509-515, July.
    17. Marlin W. Ulmer & Alan Erera & Martin Savelsbergh, 2022. "Dynamic service area sizing in urban delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 763-793, September.
    18. Lodree Jr., Emmett J. & Uzochukwu, Benedict M., 2008. "Production planning for a deteriorating item with stochastic demand and consumer choice," International Journal of Production Economics, Elsevier, vol. 116(2), pages 219-232, December.
    19. Lin, J. & Z. & Zhang, Z., 2018. "Does transportation matter to tobacco leaf producers? Evidence from Guizhou, China," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 276998, International Association of Agricultural Economists.
    20. Hugo P. Simão & Jeff Day & Abraham P. George & Ted Gifford & John Nienow & Warren B. Powell, 2009. "An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application," Transportation Science, INFORMS, vol. 43(2), pages 178-197, May.

    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:jmathe:v:10:y:2022:i:21:p:3933-:d:950914. 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.