IDEAS home Printed from https://ideas.repec.org/a/inm/orinte/v54y2024i5p417-430.html
   My bibliography  Save this article

Smart Parcel Consolidation at Cainiao

Author

Listed:
  • Yujie Chen

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Biao Yuan

    (Data-Driven Management Decision-Making Laboratory, Shanghai Jiao Tong University, Shanghai 200030, China; Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, China)

  • Yinzhi Zhou

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Yuwei Chen

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

  • Haoyuan Hu

    (Cainiao Network, Hangzhou, Zhejiang 311100, China)

Abstract

Cainiao proposes a novel business model that consolidates parcels ordered by the same consumer from one or more merchants during the fulfillment process. The objective is to increase delivery speed without incurring additional costs for merchants and consumers. To support this business model, we develop three analytics methods: (1) a two-phase online optimization algorithm to determine which of a consumer’s parcels constitute consolidated parcels and to select the shipping methods for the consolidated parcels that maximize the gains while satisfying the constraints (e.g., the 10-day on-time delivery rate of all consumer parcels created within a specified time should reach a target value), (2) a statistical method to calculate delivery time distributions to obtain on-time delivery rates within different days, and (3) a simulation-based optimization method to guide managers in setting appropriate target values for the constraints. In addition, we prove that the expected optimality gap and constraint violation of the online optimization algorithm have sublinear bounds, and we validate its effectiveness and robustness by testing instances generated from real-world data. Since 2020, Cainiao has utilized the system to consolidate numerous parcels shipped from China to more than 50 countries and regions, thus saving tens of millions of dollars annually and reducing delivery time by at least 50%.

Suggested Citation

  • Yujie Chen & Biao Yuan & Yinzhi Zhou & Yuwei Chen & Haoyuan Hu, 2024. "Smart Parcel Consolidation at Cainiao," Interfaces, INFORMS, vol. 54(5), pages 417-430, September.
    • Handle: RePEc:inm:orinte:v:54:y:2024:i:5:p:417-430
    DOI: 10.1287/inte.2024.0124
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/inte.2024.0124
    Download Restriction: no
    File URL: https://libkey.io/10.1287/inte.2024.0124?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. Feng Li & Zhou Xu & Zhi-Long Chen, 2020. "Production and Transportation Integration for Commit-to-Delivery Mode with General Shipping Costs," INFORMS Journal on Computing, INFORMS, vol. 32(4), pages 1012-1029, October.
    2. James K. Higginson & James H. Bookbinder, 1995. "Markovian Decision Processes in Shipment Consolidation," Transportation Science, INFORMS, vol. 29(3), pages 242-255, August.
    3. Chung-Yee Lee & Sila Çetinkaya & Wikrom Jaruphongsa, 2003. "A Dynamic Model for Inventory Lot Sizing and Outbound Shipment Scheduling at a Third-Party Warehouse," Operations Research, INFORMS, vol. 51(5), pages 735-747, October.
    4. Stefanus Jasin, 2015. "Performance of an LP-Based Control for Revenue Management with Unknown Demand Parameters," Operations Research, INFORMS, vol. 63(4), pages 909-915, August.
    5. Arash Asadpour & Xuan Wang & Jiawei Zhang, 2020. "Online Resource Allocation with Limited Flexibility," Management Science, INFORMS, vol. 66(2), pages 642-666, February.
    6. Lap Mui Ann Chan & Ana Muriel & Zuo-Jun Shen & David Simchi-Levi, 2002. "On the Effectiveness of Zero-Inventory-Ordering Policies for the Economic Lot-Sizing Model with a Class of Piecewise Linear Cost Structures," Operations Research, INFORMS, vol. 50(6), pages 1058-1067, December.
    7. Shipra Agrawal & Zizhuo Wang & Yinyu Ye, 2014. "A Dynamic Near-Optimal Algorithm for Online Linear Programming," Operations Research, INFORMS, vol. 62(4), pages 876-890, August.
    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. Wang, Julong & Liu, Zhixue & Li, Feng, 2024. "Integrated production and transportation scheduling problem under nonlinear cost structures," European Journal of Operational Research, Elsevier, vol. 313(3), pages 883-904.
    2. Chung‐Lun Li & Jinwen Ou & Vernon N. Hsu, 2012. "Dynamic lot sizing with all‐units discount and resales," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(3‐4), pages 230-243, April.
    3. Nguyen, Christine & Dessouky, Maged & Toriello, Alejandro, 2014. "Consolidation strategies for the delivery of perishable products," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 108-121.
    4. Chung-Lun Li & Vernon Ning Hsu & Wen-Qiang Xiao, 2004. "Dynamic Lot Sizing with Batch Ordering and Truckload Discounts," Operations Research, INFORMS, vol. 52(4), pages 639-654, August.
    5. Negin Gorlezaei & Patrick Jaillet & Zijie Zhou, 2022. "Online Resource Allocation with Samples," Papers 2210.04774, arXiv.org.
    6. Yang, Xianyan & Li, Feng & Liu, Zhixue & Xu, Zhou, 2024. "New exact and heuristic algorithms for general production and delivery integration," European Journal of Operational Research, Elsevier, vol. 316(2), pages 419-442.
    7. Akbalik, A. & Pochet, Y., 2009. "Valid inequalities for the single-item capacitated lot sizing problem with step-wise costs," European Journal of Operational Research, Elsevier, vol. 198(2), pages 412-434, October.
    8. Wei, Bo & Çetinkaya, Sıla & Cline, Daren B.H., 2023. "Inbound replenishment and outbound dispatch decisions under hybrid shipment consolidation policies: An analytical model and comparison," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    9. Dawsen Hwang & Patrick Jaillet & Vahideh Manshadi, 2021. "Online Resource Allocation Under Partially Predictable Demand," Operations Research, INFORMS, vol. 69(3), pages 895-915, May.
    10. Brahimi, Nadjib & Absi, Nabil & Dauzère-Pérès, Stéphane & Nordli, Atle, 2017. "Single-item dynamic lot-sizing problems: An updated survey," European Journal of Operational Research, Elsevier, vol. 263(3), pages 838-863.
    11. Dinçer Konur & Joseph Geunes, 2019. "Integrated districting, fleet composition, and inventory planning for a multi-retailer distribution system," Annals of Operations Research, Springer, vol. 273(1), pages 527-559, February.
    12. Yue Jin & Ana Muriel, 2009. "Single‐warehouse multi‐retailer inventory systems with full truckload shipments," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(5), pages 450-464, August.
    13. Mutlu, Fatih & Çetinkaya, Sıla, 2020. "Supplier–carrier–buyer channels: Contractual pricing for a carrier serving a supplier–buyer partnership," International Journal of Production Economics, Elsevier, vol. 230(C).
    14. Satır, Benhür & Erenay, Fatih Safa & Bookbinder, James H., 2018. "Shipment consolidation with two demand classes: Rationing the dispatch capacity," European Journal of Operational Research, Elsevier, vol. 270(1), pages 171-184.
    15. Stan van Hoesel & H. Edwin Romeijn & Dolores Romero Morales & Albert P. M. Wagelmans, 2005. "Integrated Lot Sizing in Serial Supply Chains with Production Capacities," Management Science, INFORMS, vol. 51(11), pages 1706-1719, November.
    16. Sara Martins & Pedro Amorim & Bernardo Almada-Lobo, 2018. "Delivery mode planning for distribution to brick-and-mortar retail stores: discussion and literature review," Flexible Services and Manufacturing Journal, Springer, vol. 30(4), pages 785-812, December.
    17. Yuhang Ma & Paat Rusmevichientong & Mika Sumida & Huseyin Topaloglu, 2020. "An Approximation Algorithm for Network Revenue Management Under Nonstationary Arrivals," Operations Research, INFORMS, vol. 68(3), pages 834-855, May.
    18. König, Eva & Schön, Cornelia, 2021. "Railway delay management with passenger rerouting considering train capacity constraints," European Journal of Operational Research, Elsevier, vol. 288(2), pages 450-465.
    19. Perera, Sandun & Janakiraman, Ganesh & Niu, Shun-Chen, 2017. "Optimality of (s, S) policies in EOQ models with general cost structures," International Journal of Production Economics, Elsevier, vol. 187(C), pages 216-228.
    20. Mohammad Reza Aminian & Vahideh Manshadi & Rad Niazadeh, 2025. "Markovian Search with Socially Aware Constraints," Papers 2501.13346, arXiv.org.

    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:54:y:2024:i:5:p:417-430. 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.