IDEAS home Printed from https://ideas.repec.org/a/inm/ormsom/v26y2024i1p291-311.html

The Driver-Aide Problem: Coordinated Logistics for Last-Mile Delivery

Author

Listed:
  • S. Raghavan

    (Robert H. Smith School of Business and Institute for Systems Research, University of Maryland, College Park, Maryland 20742)

  • Rui Zhang

    (Leeds School of Business, University of Colorado Boulder, Boulder, Colorado 80309)

Abstract

Problem definition : Last-mile delivery is a critical component of logistics networks, accounting for approximately 30%–35% of costs. As delivery volumes have increased, truck route times have become unsustainably long. To address this issue, many logistics companies, including FedEx and UPS, have resorted to using a “driver aide” to assist with deliveries. The aide can assist the driver in two ways. As a “jumper,” the aide works with the driver in preparing and delivering packages, thus reducing the service time at a given stop. As a “helper,” the aide can independently work at a location delivering packages, and the driver can leave to deliver packages at other locations and then return. Given a set of delivery locations, travel times, service times, jumper’s savings, and helper’s service times, the goal is to determine both the delivery route and the most effective way to use the aide (e.g., sometimes as a jumper and sometimes as a helper) to minimize the total routing time. Methodology/results : We model this problem as an integer program with an exponential number of variables and an exponential number of constraints and propose a branch-cut-and-price approach for solving it. Our computational experiments are based on simulated instances built on real-world data provided by an industrial partner and a data set released by Amazon. The instances based on the Amazon data set show that this novel operation can lead to, on average, a 35.8% reduction in routing time and 22.0% in cost savings. More importantly, our results characterize the conditions under which this novel operation mode can lead to significant savings in terms of both the routing time and cost. Managerial implications : Our computational results show that the driver aide with both jumper and helper modes is most effective when there are denser service regions and when the truck’s speed is higher (≥10 miles per hour). Coupled with an economic analysis, we come up with rules of thumb (that have close to 100% accuracy) to predict whether to use the aide and in which mode. Empirically, we find that the service delivery routes with greater than 50% of the time devoted to delivery (as opposed to driving) are the ones that provide the greatest benefit. These routes are characterized by a high density of delivery locations.

Suggested Citation

  • S. Raghavan & Rui Zhang, 2024. "The Driver-Aide Problem: Coordinated Logistics for Last-Mile Delivery," Manufacturing & Service Operations Management, INFORMS, vol. 26(1), pages 291-311, January.
  • Handle: RePEc:inm:ormsom:v:26:y:2024:i:1:p:291-311
    DOI: 10.1287/msom.2022.0211
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/msom.2022.0211
    Download Restriction: no

    File URL: https://libkey.io/10.1287/msom.2022.0211?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. Coindreau, Marc-Antoine & Gallay, Olivier & Zufferey, Nicolas, 2019. "Vehicle routing with transportable resources: Using carpooling and walking for on-site services," European Journal of Operational Research, Elsevier, vol. 279(3), pages 996-1010.
    2. Luciano Costa & Claudio Contardo & Guy Desaulniers, 2019. "Exact Branch-Price-and-Cut Algorithms for Vehicle Routing," Transportation Science, INFORMS, vol. 53(4), pages 946-985, July.
    3. Ulrich Pferschy & Rostislav Staněk, 2017. "Generating subtour elimination constraints for the TSP from pure integer solutions," 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. 25(1), pages 231-260, March.
    4. A. Mor & M. G. Speranza, 2022. "Vehicle routing problems over time: a survey," Annals of Operations Research, Springer, vol. 314(1), pages 255-275, July.
    5. Alexandre M. Florio & Richard F. Hartl & Stefan Minner, 2020. "New Exact Algorithm for the Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 54(4), pages 1073-1090, July.
    6. Cynthia Barnhart & Ellis L. Johnson & George L. Nemhauser & Martin W. P. Savelsbergh & Pamela H. Vance, 1998. "Branch-and-Price: Column Generation for Solving Huge Integer Programs," Operations Research, INFORMS, vol. 46(3), pages 316-329, June.
    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. He, Xueting & Zhen, Lu, 2025. "Column-and-row generation based exact algorithm for relay-based on-demand delivery systems," Transportation Research Part B: Methodological, Elsevier, vol. 196(C).
    2. Amaya, Johanna & Reed, Sara, 2025. "Space management policy for urban last-mile parking infrastructure: A demand-oriented approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 200(C).

    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. Yang, Jinfeng & Li, Jiliu & Qin, Hu & Su, E & Zhang, Rui, 2026. "The freight multimodal transport problem with buses and drones: An integrated approach for last-mile delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 206(C).
    2. Rui Zhang, 2025. "Branch-and-Price for the Capacitated Autonomous Vehicle Assisted Delivery Problem," INFORMS Journal on Computing, INFORMS, vol. 37(5), pages 1328-1349, September.
    3. Jiliu Li & Zhixing Luo & Roberto Baldacci & Hu Qin & Zhou Xu, 2023. "A New Exact Algorithm for Single-Commodity Vehicle Routing with Split Pickups and Deliveries," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 31-49, January.
    4. Yu Yang, 2025. "DeLuxing: Deep Lagrangian Underestimate Fixing for Column-Generation-Based Exact Methods," Operations Research, INFORMS, vol. 73(3), pages 1184-1207, May.
    5. Florio, Alexandre M. & Gendreau, Michel & Hartl, Richard F. & Minner, Stefan & Vidal, Thibaut, 2023. "Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1081-1093.
    6. Archetti, C. & Coelho, L.C. & Speranza, M.G. & Vansteenwegen, P., 2026. "Beyond fifty years of vehicle routing: Insights into the history and the future," European Journal of Operational Research, Elsevier, vol. 330(2), pages 355-372.
    7. Alexandre M. Florio & Nabil Absi & Dominique Feillet, 2021. "Routing Electric Vehicles on Congested Street Networks," Transportation Science, INFORMS, vol. 55(1), pages 238-256, 1-2.
    8. Hoogendoorn, Y.N. & Dalmeijer, K., 2021. "Resource-robust valid inequalities for set covering and set partitioning models," Econometric Institute Research Papers EI 2020-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Mahtab, Zuhayer & Hu, Shichun & Dessouky, Maged & Ordoñez, Fernando, 2025. "The ridesharing routing problem with flexible pickup and drop-off points," Transportation Research Part B: Methodological, Elsevier, vol. 198(C).
    10. Sonntag, Danja R. & Schrotenboer, Albert H. & Kiesmüller, Gudrun P., 2023. "Stochastic inventory routing with time-based shipment consolidation," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1186-1201.
    11. Antoine Legrain & Jérémy Omer, 2024. "A Dedicated Pricing Algorithm to Solve a Large Family of Nurse Scheduling Problems with Branch-and-Price," INFORMS Journal on Computing, INFORMS, vol. 36(4), pages 1108-1128, July.
    12. Matteo Petris & Claudia Archetti & Diego Cattaruzza & Maxime Ogier & Frédéric Semet, 2025. "A tutorial on Branch-Price-and-Cut algorithms," 4OR, Springer, vol. 23(1), pages 1-52, March.
    13. Natasja Sluijk & Alexandre M. Florio & Joris Kinable & Nico Dellaert & Tom Van Woensel, 2023. "A Chance-Constrained Two-Echelon Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 57(1), pages 252-272, January.
    14. Jiachen Zhang & Youcef Magnouche & Pierre Bauguion & Sebastien Martin & J. Christopher Beck, 2024. "Computing Bipath Multicommodity Flows with Constraint Programming–Based Branch-and-Price-and-Cut," INFORMS Journal on Computing, INFORMS, vol. 36(6), pages 1634-1653, December.
    15. Miao Yu & Viswanath Nagarajan & Siqian Shen, 2022. "Improving Column Generation for Vehicle Routing Problems via Random Coloring and Parallelization," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 953-973, March.
    16. Marjolein Aerts-Veenstra & Marilène Cherkesly & Timo Gschwind, 2024. "A Unified Branch-Price-and-Cut Algorithm for Multicompartment Pickup and Delivery Problems," Transportation Science, INFORMS, vol. 58(5), pages 1121-1142, September.
    17. 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.
    18. Cobeña, Brenda & Contardo, Claudio, 2026. "Column generation and local search for the profit-oriented hub-line location problem with elastic demands," Omega, Elsevier, vol. 138(C).
    19. Baldacci, Roberto & Hoshino, Edna A. & Hill, Alessandro, 2023. "New pricing strategies and an effective exact solution framework for profit-oriented ring arborescence problems," European Journal of Operational Research, Elsevier, vol. 307(2), pages 538-553.
    20. Ines Mathlouthi & Michel Gendreau & Jean-Yves Potvin, 2021. "Branch-and-Price for a Multi-attribute Technician Routing and Scheduling Problem," SN Operations Research Forum, Springer, vol. 2(1), pages 1-35, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:ormsom:v:26:y:2024:i:1:p:291-311. 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.