IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v52y2022i1p27-41.html
   My bibliography  Save this article

Alibaba Vehicle Routing Algorithms Enable Rapid Pick and Delivery

Author

Listed:
  • Haoyuan Hu

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Ying Zhang

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Jiangwen Wei

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Yang Zhan

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Xinhui Zhang

    (Alibaba Group, Hangzhou, Zhejiang 310052, China)

  • Shaojian Huang

    (Alibaba Group, Hangzhou, Zhejiang 310052, China)

  • Guangrui Ma

    (Alibaba Group, Hangzhou, Zhejiang 310052, China)

  • Yuming Deng

    (Alibaba Group, Hangzhou, Zhejiang 310052, China)

  • Siwei Jiang

    (Lazada Group, Singapore 068811)

Abstract

Alibaba Group pioneered integrated online and offline retail models to allow customers to place online orders of e-commerce and grocery products at its participating stores or restaurants for rapid delivery—in some cases, in as little as 30 minutes after an order has been placed. To meet these service commitments, quick online routing decisions must be made to optimize order picking routes at warehouses and delivery routes for drivers. The solutions to these routing problems are complicated by stringent service commitments, uncertainties, and complex operations in warehouses with limited space. Alibaba has developed a set of algorithms for vehicle routing problems (VRPs), which include an open-architecture adaptive large neighborhood search to support the solution of variants of routing problems and a deep learning-based approach that trains neural network models offline to generate almost instantaneous solutions online. These algorithms have been implemented to solve VRPs in several Alibaba subsidiaries, have generated more than $50 million in annual financial savings, and are applicable to the broader logistics industry. The success of these algorithms has fermented an inner-source community of operations researchers within Alibaba, boosted the confidence of the company’s executives in operations research, and made operations research one of the core competencies of Alibaba Group.

Suggested Citation

  • Haoyuan Hu & Ying Zhang & Jiangwen Wei & Yang Zhan & Xinhui Zhang & Shaojian Huang & Guangrui Ma & Yuming Deng & Siwei Jiang, 2022. "Alibaba Vehicle Routing Algorithms Enable Rapid Pick and Delivery," Interfaces, INFORMS, vol. 52(1), pages 27-41, January.
    • Handle: RePEc:inm:orinte:v:52:y:2022:i:1:p:27-41
    DOI: 10.1287/inte.2021.1108
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2021.1108
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2021.1108?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
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Patrick Jaillet, & Sébastien Martin, 2019. "Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications," Operations Research, INFORMS, vol. 67(1), pages 143-162, January.
    2. David Pisinger & Stefan Ropke, 2010. "Large Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 399-419, Springer.
    3. Dimitris Bertsimas & Arthur Delarue & William Eger & John Hanlon & Sebastien Martin, 2020. "Bus Routing Optimization Helps Boston Public Schools Design Better Policies," Interfaces, INFORMS, vol. 50(1), pages 37-49, January.
    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. Chuck Holland & Jack Levis & Ranganath Nuggehalli & Bob Santilli & Jeff Winters, 2017. "UPS Optimizes Delivery Routes," Interfaces, INFORMS, vol. 47(1), pages 8-23, February.
    6. B. L. Hollis & P. J. Green, 2012. "Real-Life Vehicle Routing With Time Windows For Visual Attractiveness And Operational Robustness," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 29(04), pages 1-29.
    7. Véronique François & Yasemin Arda & Yves Crama, 2019. "Adaptive Large Neighborhood Search for Multitrip Vehicle Routing with Time Windows," Transportation Science, INFORMS, vol. 53(6), pages 1706-1730, November.
    8. Marlin W. Ulmer & Barrett W. Thomas & Ann Melissa Campbell & Nicholas Woyak, 2021. "The Restaurant Meal Delivery Problem: Dynamic Pickup and Delivery with Deadlines and Random Ready Times," Transportation Science, INFORMS, vol. 55(1), pages 75-100, 1-2.
    9. Goos Kant & Michael Jacks & Corné Aantjes, 2008. "Coca-Cola Enterprises Optimizes Vehicle Routes for Efficient Product Delivery," Interfaces, INFORMS, vol. 38(1), pages 40-50, February.
    10. Gilbert Laporte & Roberto Musmanno & Francesca Vocaturo, 2010. "An Adaptive Large Neighbourhood Search Heuristic for the Capacitated Arc-Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 44(1), pages 125-135, February.
    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. Yi Wang & Yafei Yang & Zhaoxiang Qin & Yefei Yang & Jun Li, 2023. "A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management," Sustainability, MDPI, vol. 15(11), pages 1-18, May.

    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. 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.
    2. Gläser, Sina, 2022. "A waste collection problem with service type option," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1216-1230.
    3. 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.
    4. Liu, Chuanju & Zhang, Junlong & Lin, Shaochong & Shen, Zuo-Jun Max, 2023. "Service network design with consistent multiple trips," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    5. Bongiovanni, Claudia & Kaspi, Mor & Cordeau, Jean-François & Geroliminis, Nikolas, 2022. "A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    6. Bach, Lukas & Hasle, Geir & Schulz, Christian, 2019. "Adaptive Large Neighborhood Search on the Graphics Processing Unit," European Journal of Operational Research, Elsevier, vol. 275(1), pages 53-66.
    7. Arpan Rijal & Marco Bijvank & Asvin Goel & René de Koster, 2021. "Workforce Scheduling with Order-Picking Assignments in Distribution Facilities," Transportation Science, INFORMS, vol. 55(3), pages 725-746, May.
    8. Braekers, Kris & Hartl, Richard F. & Parragh, Sophie N. & Tricoire, Fabien, 2016. "A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience," European Journal of Operational Research, Elsevier, vol. 248(2), pages 428-443.
    9. Mo, Pengli & Yao, Yu & D’Ariano, Andrea & Liu, Zhiyuan, 2023. "The vehicle routing problem with underground logistics: Formulation and algorithm," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    10. Özarık, Sami Serkan & Veelenturf, Lucas P. & Woensel, Tom Van & Laporte, Gilbert, 2021. "Optimizing e-commerce last-mile vehicle routing and scheduling under uncertain customer presence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 148(C).
    11. Hiermann, Gerhard & Puchinger, Jakob & Ropke, Stefan & Hartl, Richard F., 2016. "The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations," European Journal of Operational Research, Elsevier, vol. 252(3), pages 995-1018.
    12. Matusiak, M. & de Koster, M.B.M. & Saarinen, J., 2015. "Data-driven warehouse optimization," ERIM Report Series Research in Management ERS-2015-008-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Masson, Renaud & Lahrichi, Nadia & Rousseau, Louis-Martin, 2016. "A two-stage solution method for the annual dairy transportation problem," European Journal of Operational Research, Elsevier, vol. 251(1), pages 36-43.
    14. Timo Hintsch, 2019. "Large Multiple Neighborhood Search for the Soft-Clustered Vehicle-Routing Problem," Working Papers 1904, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    15. Ulrike Ritzinger & Jakob Puchinger & Richard Hartl, 2016. "Dynamic programming based metaheuristics for the dial-a-ride problem," Annals of Operations Research, Springer, vol. 236(2), pages 341-358, January.
    16. Vadlamani, Satish & Hosseini, Seyedmohsen, 2014. "A novel heuristic approach for solving aircraft landing problem with single runway," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 144-148.
    17. 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).
    18. Timo Gschwind & Michael Drexl, 2016. "Adaptive Large Neighborhood Search with a Constant-Time Feasibility Test for the Dial-a-Ride Problem," Working Papers 1624, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    19. Ruf, Moritz & Cordeau, Jean-François, 2021. "Adaptive large neighborhood search for integrated planning in railroad classification yards," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 26-51.
    20. Duan, Gang & Aghalari, Amin & Chen, Li & Marufuzzaman, Mohammad & Ma, Junfeng, 2021. "Vessel routing optimization for floating macro-marine debris collection in the ocean considering dynamic velocity and direction," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(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:inm:orinte:v:52:y:2022:i:1:p:27-41. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.