IDEAS home Printed from https://ideas.repec.org/a/kap/netspa/v24y2024i4d10.1007_s11067-024-09643-1.html
   My bibliography  Save this article

Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations

Author

Listed:
  • Surendra Reddy Kancharla

    (“FRIEDRICH LIST” Faculty of Transport and Traffic Sciences, Dresden University of Technology)

  • Tom Woensel

    (Eindhoven University of Technology)

  • S. Travis Waller

    (“FRIEDRICH LIST” Faculty of Transport and Traffic Sciences, Dresden University of Technology
    Australian National University)

  • Satish V. Ukkusuri

    (Purdue University)

Abstract

We investigate the Meal Delivery Routing Problem (MDRP), managing courier assignments between restaurants and customers. Our proposed variant considers uncertainties in meal preparation times and future order numbers with their locations, mirroring real challenges meal delivery providers face. Employing a rolling-horizon framework integrating Sample Average Approximation (SAA) and the Adaptive Large Neighborhood Search (ALNS) algorithm, we analyze modified Grubhub MDRP instances. Considering route planning uncertainties, our approach identifies routes at least 25% more profitable than deterministic methods reliant on expected values. Our study underscores the pivotal role of efficient meal preparation time management, impacting order rejections, customer satisfaction, and operational efficiency.

Suggested Citation

  • Surendra Reddy Kancharla & Tom Woensel & S. Travis Waller & Satish V. Ukkusuri, 2024. "Meal Delivery Routing Problem with Stochastic Meal Preparation Times and Customer Locations," Networks and Spatial Economics, Springer, vol. 24(4), pages 997-1020, December.
    • Handle: RePEc:kap:netspa:v:24:y:2024:i:4:d:10.1007_s11067-024-09643-1
    DOI: 10.1007/s11067-024-09643-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11067-024-09643-1
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11067-024-09643-1?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. Ramon Faganello Fachini & Vinícius Amaral Armentano & Franklina Maria Bragion Toledo, 2022. "A Granular Local Search Matheuristic for a Heterogeneous Fleet Vehicle Routing Problem with Stochastic Travel Times," Networks and Spatial Economics, Springer, vol. 22(1), pages 33-64, March.
    2. Li, Yuan & Chen, Haoxun & Prins, Christian, 2016. "Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests," European Journal of Operational Research, Elsevier, vol. 252(1), pages 27-38.
    3. Ampol Karoonsoontawong & Puntipa Punyim & Wanvara Nueangnitnaraporn & Vatanavongs Ratanavaraha, 2020. "Multi-Trip Time-Dependent Vehicle Routing Problem with Soft Time Windows and Overtime Constraints," Networks and Spatial Economics, Springer, vol. 20(2), pages 549-598, June.
    4. 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.
    5. Györgyi, Péter & Kis, Tamás, 2019. "A probabilistic approach to pickup and delivery problems with time window uncertainty," European Journal of Operational Research, Elsevier, vol. 274(3), pages 909-923.
    6. Gilbert Laporte & FranÇois V. Louveaux & Luc van Hamme, 2002. "An Integer L -Shaped Algorithm for the Capacitated Vehicle Routing Problem with Stochastic Demands," Operations Research, INFORMS, vol. 50(3), pages 415-423, June.
    7. Zhang, Jian & Luo, Kelin & Florio, Alexandre M. & Van Woensel, Tom, 2023. "Solving large-scale dynamic vehicle routing problems with stochastic requests," European Journal of Operational Research, Elsevier, vol. 306(2), pages 596-614.
    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. Frey, Christian M.M. & Jungwirth, Alexander & Frey, Markus & Kolisch, Rainer, 2023. "The vehicle routing problem with time windows and flexible delivery locations," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1142-1159.
    2. Hammami, Farouk & Rekik, Monia & Coelho, Leandro C., 2019. "Exact and heuristic solution approaches for the bid construction problem in transportation procurement auctions with a heterogeneous fleet," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 150-177.
    3. Shejun Deng & Yingying Yuan & Yong Wang & Haizhong Wang & Charles Koll, 2020. "Collaborative multicenter logistics delivery network optimization with resource sharing," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-31, November.
    4. 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.
    5. Margaretha Gansterer & Richard F. Hartl & Sarah Wieser, 2021. "Assignment constraints in shared transportation services," Annals of Operations Research, Springer, vol. 305(1), pages 513-539, October.
    6. Zhu, Lin & Sheu, Jiuh-Biing, 2018. "Failure-specific cooperative recourse strategy for simultaneous pickup and delivery problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 271(3), pages 896-912.
    7. Parada, Lucas & Legault, Robin & Côté, Jean-François & Gendreau, Michel, 2024. "A disaggregated integer L-shaped method for stochastic vehicle routing problems with monotonic recourse," European Journal of Operational Research, Elsevier, vol. 318(2), pages 520-533.
    8. Dong, Xiaotong & Chow, Joseph Y.J. & Waller, S. Travis & Rey, David, 2022. "A chance-constrained dial-a-ride problem with utility-maximising demand and multiple pricing structures," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    9. Huang, Baobin & Tang, Lixin & Baldacci, Roberto & Wang, Gongshu & Sun, Defeng, 2023. "A metaheuristic algorithm for a locomotive routing problem arising in the steel industry," European Journal of Operational Research, Elsevier, vol. 308(1), pages 385-399.
    10. Jean-François Côté & Michel Gendreau & Jean-Yves Potvin, 2020. "The Vehicle Routing Problem with Stochastic Two-Dimensional Items," Transportation Science, INFORMS, vol. 54(2), pages 453-469, March.
    11. 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.
    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. 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.
    14. Lai, Minghui & Cai, Xiaoqiang & Hu, Qian, 2017. "An iterative auction for carrier collaboration in truckload pickup and delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 107(C), pages 60-80.
    15. Eilertsen, Ulrik & Falck-Pedersen, Olav M. & Henriksen, Jone V. & Fagerholt, Kjetil & Pantuso, Giovanni, 2024. "Joint relocation and pricing in electric car-sharing systems," European Journal of Operational Research, Elsevier, vol. 315(2), pages 553-566.
    16. Lyu, Xiaohui & Chen, Haoxun & Wang, Nengmin & Yang, Zhen, 2019. "A multi-round exchange mechanism for carrier collaboration in less than truckload transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 38-59.
    17. 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.
    18. Ghilas, Veaceslav & Demir, Emrah & Woensel, Tom Van, 2016. "A scenario-based planning for the pickup and delivery problem with time windows, scheduled lines and stochastic demands," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 34-51.
    19. Yin, Jiateng & D’Ariano, Andrea & Wang, Yihui & Yang, Lixing & Tang, Tao, 2021. "Timetable coordination in a rail transit network with time-dependent passenger demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 183-202.
    20. Sun, Peng & Veelenturf, Lucas P. & Hewitt, Mike & Van Woensel, Tom, 2020. "Adaptive large neighborhood search for the time-dependent profitable pickup and delivery problem with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(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:kap:netspa:v:24:y:2024:i:4:d:10.1007_s11067-024-09643-1. 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.