IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v20y2020i2d10.1007_s12351-017-0363-0.html
   My bibliography  Save this article

Disturbance management for vehicle routing with time window changes

Author

Listed:
  • Hualong Yang

    (Dalian Maritime University)

  • Liang Zhao

    (Dalian Maritime University)

  • Di Ye

    (Dalian Maritime University)

  • Jiangshan Ma

    (Shanghai Maritime University)

Abstract

In this paper, the issue of vehicle routing with time window changes is addressed. Considering the uncertainty of customers’ time windows in distribution activities, this paper used the theory of disturbance management. The objective is to minimize the negative impacts of the perturbation attributed to time window changes. The identification of time window change that would cause a perturbation to the current distribution plan was analyzed. In order to measure the negative impact, three metrics of disturbance were analyzed in this paper, including path deviation, service time deviation and cost deviation. Based on vehicles’ positions at the disturbance time, a disturbance recovery model regarding to time window changes of customers is established. A dispatching method that is based on tabu search was proposed to obtain a timely and optimal solution. Finally, the computational experiments indicate that the proposed method is feasible for solving this real-word problem and is more effective than other incident-handling methods.

Suggested Citation

  • Hualong Yang & Liang Zhao & Di Ye & Jiangshan Ma, 2020. "Disturbance management for vehicle routing with time window changes," Operational Research, Springer, vol. 20(2), pages 1093-1112, June.
    • Handle: RePEc:spr:operea:v:20:y:2020:i:2:d:10.1007_s12351-017-0363-0
    DOI: 10.1007/s12351-017-0363-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-017-0363-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12351-017-0363-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ferrucci, Francesco & Bock, Stefan & Gendreau, Michel, 2013. "A pro-active real-time control approach for dynamic vehicle routing problems dealing with the delivery of urgent goods," European Journal of Operational Research, Elsevier, vol. 225(1), pages 130-141.
    2. Schyns, M., 2015. "An ant colony system for responsive dynamic vehicle routing," European Journal of Operational Research, Elsevier, vol. 245(3), pages 704-718.
    3. 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.
    4. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fang Zhao & Bingfeng Si & Zhenlin Wei & Tianwei Lu, 2023. "Time-dependent vehicle routing problem of perishable product delivery considering the differences among paths on the congested road," Operational Research, Springer, vol. 23(1), pages 1-23, March.

    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. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    2. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    3. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    4. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    5. Aderemi Oluyinka Adewumi & Olawale Joshua Adeleke, 2018. "A survey of recent advances in vehicle routing problems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 155-172, February.
    6. 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.
    7. Mohamed Cissé & Semih Yalçindag & Yannick Kergosien & Evren Sahin & Christophe Lenté & Andrea Matta, 2017. "OR problems related to Home Health Care: A review of relevant routing and scheduling problems," Post-Print hal-01736714, HAL.
    8. 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.
    9. Roman Buil & Jesica de Armas & Daniel Riera & Sandra Orozco, 2021. "Optimization of the Real-Time Response to Roadside Incidents through Heuristic and Linear Programming," Mathematics, MDPI, vol. 9(16), pages 1-20, August.
    10. Ritzinger, Ulrike & Puchinger, Jakob & Rudloff, Christian & Hartl, Richard F., 2022. "Comparison of anticipatory algorithms for a dial-a-ride problem," European Journal of Operational Research, Elsevier, vol. 301(2), pages 591-608.
    11. Yan Cheng Hsu & Jose L. Walteros & Rajan Batta, 2020. "Solving the petroleum replenishment and routing problem with variable demands and time windows," Annals of Operations Research, Springer, vol. 294(1), pages 9-46, November.
    12. Ioannou, Petros & Giuliano, Genevieve & Dessouky, Maged & Chen, Pengfei & Dexter, Sue, 2020. "Freight Load Balancing and Efficiencies in Alternative Fuel Freight Modes," Institute of Transportation Studies, Working Paper Series qt3ns4b894, Institute of Transportation Studies, UC Davis.
    13. 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.
    14. Ji, Chenlu & Mandania, Rupal & Liu, Jiyin & Liret, Anne, 2022. "Scheduling on-site service deliveries to minimise the risk of missing appointment times," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    15. 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.
    16. van Engelen, Matti & Cats, Oded & Post, Henk & Aardal, Karen, 2018. "Enhancing flexible transport services with demand-anticipatory insertion heuristics," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 110(C), pages 110-121.
    17. Bock, Stefan, 2024. "Vehicle routing for connected service areas - a versatile approach covering single, hierarchical, and bi-criteria objectives," European Journal of Operational Research, Elsevier, vol. 313(3), pages 905-925.
    18. John E. Fontecha & Oscar O. Guaje & Daniel Duque & Raha Akhavan-Tabatabaei & Juan P. Rodríguez & Andrés L. Medaglia, 2020. "Combined maintenance and routing optimization for large-scale sewage cleaning," Annals of Operations Research, Springer, vol. 286(1), pages 441-474, March.
    19. Yuxin Liu & Zihang Qin & Jin Liu, 2023. "An Improved Genetic Algorithm for the Granularity-Based Split Vehicle Routing Problem with Simultaneous Delivery and Pickup," Mathematics, MDPI, vol. 11(15), pages 1-15, July.
    20. Xian Cheng & Shaoyi Liao & Zhongsheng Hua, 2017. "A policy of picking up parcels for express courier service in dynamic environments," International Journal of Production Research, Taylor & Francis Journals, vol. 55(9), pages 2470-2488, 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:spr:operea:v:20:y:2020:i:2:d:10.1007_s12351-017-0363-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.