IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v173y2023ics1366554523000947.html
   My bibliography  Save this article

A robust optimization approach for the vehicle routing problem with cross-docking under demand uncertainty

Author

Listed:
  • Yu, Vincent F.
  • Anh, Pham Tuan
  • Baldacci, Roberto

Abstract

This research addresses the Vehicle Routing Problem with Cross-docking under Demand Uncertainty (VRPCD-DU) where a set of homogeneous vehicles is used to transport orders from the suppliers to the corresponding customers via a cross-dock. VRPCD-DU considers customer demand volumes as random variables and determines a minimum cost delivery plan that is feasible for all anticipated demand realizations. A robust optimization counterpart of a deterministic VRPCD formulation is derived where support for the demand is a polyhedral set. The robust formulation can only be used to solve small VRPCD-DU instances, and an effective adaptive large neighborhood search (ALNS) algorithm is proposed for solving large instances. Extensive numerical experiments are conducted on benchmark sets for both VRPCD and VRPCD-DU. The results show that the ALNS algorithm computes new best-known solutions of the VRPCD benchmark instances. Moreover, demand uncertainty is extensively analyzed by investigating managerial insights. The price of robustness (PoR) is compared by considering various budget sets and alternative partitioning methods for the customers (i.e., “random” and “clustered”). The findings imply that a higher probability of capacity constraints’ violation is generally observed for the suppliers and that customer partitioning methods share similar PoR.

Suggested Citation

  • Yu, Vincent F. & Anh, Pham Tuan & Baldacci, Roberto, 2023. "A robust optimization approach for the vehicle routing problem with cross-docking under demand uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:transe:v:173:y:2023:i:c:s1366554523000947
    DOI: 10.1016/j.tre.2023.103106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554523000947
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2023.103106?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. Grangier, Philippe & Gendreau, Michel & Lehuédé, Fabien & Rousseau, Louis-Martin, 2016. "An adaptive large neighborhood search for the two-echelon multiple-trip vehicle routing problem with satellite synchronization," European Journal of Operational Research, Elsevier, vol. 254(1), pages 80-91.
    2. Stefan Ropke & David Pisinger, 2006. "An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows," Transportation Science, INFORMS, vol. 40(4), pages 455-472, November.
    3. G. B. Dantzig & J. H. Ramser, 1959. "The Truck Dispatching Problem," Management Science, INFORMS, vol. 6(1), pages 80-91, October.
    4. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    5. Chrysanthos E. Gounaris & Wolfram Wiesemann & Christodoulos A. Floudas, 2013. "The Robust Capacitated Vehicle Routing Problem Under Demand Uncertainty," Operations Research, INFORMS, vol. 61(3), pages 677-693, June.
    6. Patrick Jaillet & Jin Qi & Melvyn Sim, 2016. "Routing Optimization Under Uncertainty," Operations Research, INFORMS, vol. 64(1), pages 186-200, February.
    7. Enrico Bartolini & Dominik Goeke & Michael Schneider & Mengdie Ye, 2021. "The Robust Traveling Salesman Problem with Time Windows Under Knapsack-Constrained Travel Time Uncertainty," Transportation Science, INFORMS, vol. 55(2), pages 371-394, March.
    8. David Pisinger & Stefan Ropke, 2019. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 99-127, Springer.
    9. Pedro Munari & Alfredo Moreno & Jonathan De La Vega & Douglas Alem & Jacek Gondzio & Reinaldo Morabito, 2019. "The Robust Vehicle Routing Problem with Time Windows: Compact Formulation and Branch-Price-and-Cut Method," Transportation Science, INFORMS, vol. 53(4), pages 1043-1066, July.
    10. Yanıkoğlu, İhsan & Gorissen, Bram L. & den Hertog, Dick, 2019. "A survey of adjustable robust optimization," European Journal of Operational Research, Elsevier, vol. 277(3), pages 799-813.
    11. C Lee & K Lee & S Park, 2012. "Robust vehicle routing problem with deadlines and travel time/demand uncertainty," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 63(9), pages 1294-1306, September.
    12. Santos, Maria João & Curcio, Eduardo & Mulati, Mauro Henrique & Amorim, Pedro & Miyazawa, Flávio Keidi, 2020. "A robust optimization approach for the vehicle routing problem with selective backhauls," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    13. Lu, Da & Gzara, Fatma, 2019. "The robust vehicle routing problem with time windows: Solution by branch and price and cut," European Journal of Operational Research, Elsevier, vol. 275(3), pages 925-938.
    14. Van Belle, Jan & Valckenaers, Paul & Cattrysse, Dirk, 2012. "Cross-docking: State of the art," Omega, Elsevier, vol. 40(6), pages 827-846.
    15. Jonathan De La Vega & Pedro Munari & Reinaldo Morabito, 2019. "Robust optimization for the vehicle routing problem with multiple deliverymen," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(4), pages 905-936, December.
    16. Chrysanthos E. Gounaris & Panagiotis P. Repoussis & Christos D. Tarantilis & Wolfram Wiesemann & Christodoulos A. Floudas, 2016. "An Adaptive Memory Programming Framework for the Robust Capacitated Vehicle Routing Problem," Transportation Science, INFORMS, vol. 50(4), pages 1239-1260, November.
    17. M Wen & J Larsen & J Clausen & J-F Cordeau & G Laporte, 2009. "Vehicle routing with cross-docking," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1708-1718, December.
    18. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2012. "An adaptive large neighborhood search heuristic for the Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 223(2), pages 346-359.
    19. Yossiri Adulyasak & Jean-François Cordeau & Raf Jans, 2014. "Optimization-Based Adaptive Large Neighborhood Search for the Production Routing Problem," Transportation Science, INFORMS, vol. 48(1), pages 20-45, February.
    20. Dayarian, Iman & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2016. "An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 95-123.
    21. Reza Kiani Mavi & Mark Goh & Neda Kiani Mavi & Ferry Jie & Kerry Brown & Sharon Biermann & Ahmad A. Khanfar, 2020. "Cross-Docking: A Systematic Literature Review," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    22. Van Breedam, Alex, 1995. "Improvement heuristics for the Vehicle Routing Problem based on simulated annealing," European Journal of Operational Research, Elsevier, vol. 86(3), pages 480-490, November.
    23. Yossiri Adulyasak & Patrick Jaillet, 2016. "Models and Algorithms for Stochastic and Robust Vehicle Routing with Deadlines," Transportation Science, INFORMS, vol. 50(2), pages 608-626, May.
    24. Renaud Masson & Fabien Lehuédé & Olivier Péton, 2013. "An Adaptive Large Neighborhood Search for the Pickup and Delivery Problem with Transfers," Transportation Science, INFORMS, vol. 47(3), pages 344-355, August.
    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. Lunhao Ju & Jiang Jiang & Luofu Wu & Jianbin Sun, 2024. "A Sample Average Approximation Approach for Stochastic Optimization of Flight Test Planning with Sorties Uncertainty," Mathematics, MDPI, vol. 12(19), pages 1-20, September.

    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. Enrico Bartolini & Dominik Goeke & Michael Schneider & Mengdie Ye, 2021. "The Robust Traveling Salesman Problem with Time Windows Under Knapsack-Constrained Travel Time Uncertainty," Transportation Science, INFORMS, vol. 55(2), pages 371-394, March.
    2. Zhao, Lei & Bi, Xinhua & Li, Gendao & Dong, Zhaohui & Xiao, Ni & Zhao, Anni, 2022. "Robust traveling salesman problem with multiple drones: Parcel delivery under uncertain navigation environments," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    3. Shubhechyya Ghosal & Wolfram Wiesemann, 2020. "The Distributionally Robust Chance-Constrained Vehicle Routing Problem," Operations Research, INFORMS, vol. 68(3), pages 716-732, May.
    4. Turkeš, Renata & Sörensen, Kenneth & Hvattum, Lars Magnus, 2021. "Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search," European Journal of Operational Research, Elsevier, vol. 292(2), pages 423-442.
    5. Zhang, Li & Liu, Zhongshan & Yu, Lan & Fang, Ke & Yao, Baozhen & Yu, Bin, 2022. "Routing optimization of shared autonomous electric vehicles under uncertain travel time and uncertain service time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    6. Pedro Munari & Alfredo Moreno & Jonathan De La Vega & Douglas Alem & Jacek Gondzio & Reinaldo Morabito, 2019. "The Robust Vehicle Routing Problem with Time Windows: Compact Formulation and Branch-Price-and-Cut Method," Transportation Science, INFORMS, vol. 53(4), pages 1043-1066, July.
    7. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert, 2019. "The electric vehicle routing problem with energy consumption uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 225-255.
    8. TURKEŠ, Renata & SÖRENSEN, Kenneth & HVATTUM, Lars Magnus & BARRENA, Eva & CHENTLI, Hayet & COELHO, Leandro & DAYARIAN, Iman & GRIMAULT, Axel & GULLHAVE, Anders & IRIS, Çagatay & KESKIN, Merve & KIEFE, 2019. "Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search," Working Papers 2019002, University of Antwerp, Faculty of Business and Economics.
    9. Santos, Maria João & Curcio, Eduardo & Mulati, Mauro Henrique & Amorim, Pedro & Miyazawa, Flávio Keidi, 2020. "A robust optimization approach for the vehicle routing problem with selective backhauls," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    10. Kallestad, Jakob & Hasibi, Ramin & Hemmati, Ahmad & Sörensen, Kenneth, 2023. "A general deep reinforcement learning hyperheuristic framework for solving combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 309(1), pages 446-468.
    11. Hatzenbühler, Jonas & Jenelius, Erik & Gidófalvi, Gyözö & Cats, Oded, 2023. "Modular vehicle routing for combined passenger and freight transport," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    12. Zhang, Yimeng & Li, Xinlei & van Hassel, Edwin & Negenborn, Rudy R. & Atasoy, Bilge, 2022. "Synchromodal transport planning considering heterogeneous and vague preferences of shippers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    13. Singh, Nitish & Dang, Quang-Vinh & Akcay, Alp & Adan, Ivo & Martagan, Tugce, 2022. "A matheuristic for AGV scheduling with battery constraints," European Journal of Operational Research, Elsevier, vol. 298(3), pages 855-873.
    14. Maaike Hoogeboom & Yossiri Adulyasak & Wout Dullaert & Patrick Jaillet, 2021. "The Robust Vehicle Routing Problem with Time Window Assignments," Transportation Science, INFORMS, vol. 55(2), pages 395-413, March.
    15. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    16. Carolin Bauerhenne & Jonathan Bard & Rainer Kolisch, 2024. "Robust Routing and Scheduling of Home Healthcare Workers: A Nested Branch-and-Price Approach," Papers 2407.06215, arXiv.org.
    17. Liang Sun, 2022. "Modeling and evolutionary algorithm for solving a multi-depot mixed vehicle routing problem with uncertain travel times," Journal of Heuristics, Springer, vol. 28(5), pages 619-651, December.
    18. Liu, Yiming & Roberto, Baldacci & Zhou, Jianwen & Yu, Yang & Zhang, Yu & Sun, Wei, 2023. "Efficient feasibility checks and an adaptive large neighborhood search algorithm for the time-dependent green vehicle routing problem with time windows," European Journal of Operational Research, Elsevier, vol. 310(1), pages 133-155.
    19. Dayarian, Iman & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2016. "An adaptive large-neighborhood search heuristic for a multi-period vehicle routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 95-123.
    20. Wu, Lingxiao & Wang, Shuaian & Laporte, Gilbert, 2021. "The Robust Bulk Ship Routing Problem with Batched Cargo Selection," Transportation Research Part B: Methodological, Elsevier, vol. 143(C), pages 124-159.

    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:eee:transe:v:173:y:2023:i:c:s1366554523000947. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600244/description#description .

    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.