IDEAS home Printed from https://ideas.repec.org/a/bla/popmgt/v29y2020i9p2153-2174.html
   My bibliography  Save this article

Crowdshipping and Same‐day Delivery: Employing In‐store Customers to Deliver Online Orders

Author

Listed:
  • Iman Dayarian
  • Martin Savelsbergh

Abstract

Same‐day delivery of online orders is becoming an indispensable service for large retailers. We explore an environment in which in‐store customers supplement company drivers and deliver online orders on their way home. We consider a highly dynamic and stochastic same‐day delivery environment in which online orders as well as in‐store customers willing to make deliveries arrive throughout the day. Studying settings in which delivery capacity is uncertain is novel and practically relevant. Our proposed approaches are simple, yet produce high‐quality solutions in a short amount of time that can be employed in practice. We develop two rolling horizon dispatching approaches: a myopic one that considers only the state of the system when making decisions, and one that also incorporates probabilistic information about future online order and in‐store customer arrivals. We quantify the potential benefits of a novel form of crowdshipping for same‐day delivery and demonstrate the value of exploiting probabilistic information about the future. We explore the advantages and disadvantages of this form of crowdshipping and show the impact of changes in environment characteristics, for example, online order arrival pattern, company fleet size, and in‐store customer compensation on its performance, that is, service quality and operational cost.

Suggested Citation

  • Iman Dayarian & Martin Savelsbergh, 2020. "Crowdshipping and Same‐day Delivery: Employing In‐store Customers to Deliver Online Orders," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2153-2174, September.
    • Handle: RePEc:bla:popmgt:v:29:y:2020:i:9:p:2153-2174
    DOI: 10.1111/poms.13219
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/poms.13219
    Download Restriction: no
    File URL: https://libkey.io/10.1111/poms.13219?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. Diego Cattaruzza & Nabil Absi & Dominique Feillet, 2016. "The Multi-Trip Vehicle Routing Problem with Time Windows and Release Dates," Transportation Science, INFORMS, vol. 50(2), pages 676-693, May.
    2. Nabila Azi & Michel Gendreau & Jean-Yves Potvin, 2012. "A dynamic vehicle routing problem with multiple delivery routes," Annals of Operations Research, Springer, vol. 199(1), pages 103-112, October.
    3. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part I: Route Construction and Local Search Algorithms," Transportation Science, INFORMS, vol. 39(1), pages 104-118, February.
    4. Archetti, Claudia & Savelsbergh, Martin & Speranza, M. Grazia, 2016. "The Vehicle Routing Problem with Occasional Drivers," European Journal of Operational Research, Elsevier, vol. 254(2), pages 472-480.
    5. Olli Bräysy & Michel Gendreau, 2005. "Vehicle Routing Problem with Time Windows, Part II: Metaheuristics," Transportation Science, INFORMS, vol. 39(1), pages 119-139, February.
    6. Martin Savelsbergh & Tom Van Woensel, 2016. "50th Anniversary Invited Article—City Logistics: Challenges and Opportunities," Transportation Science, INFORMS, vol. 50(2), pages 579-590, May.
    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. Du, Jianhui & Zhang, Zhiqin & Wang, Xu & Lau, Hoong Chuin, 2023. "A hierarchical optimization approach for dynamic pickup and delivery problem with LIFO constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    2. Chen, Xinwei & Wang, Tong & Thomas, Barrett W. & Ulmer, Marlin W., 2023. "Same-day delivery with fair customer service," European Journal of Operational Research, Elsevier, vol. 308(2), pages 738-751.
    3. Yu, Vincent F. & Jodiawan, Panca & Redi, A.A.N. Perwira, 2022. "Crowd-shipping problem with time windows, transshipment nodes, and delivery options," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).
    4. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2024. "Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare," Omega, Elsevier, vol. 122(C).
    5. Di Puglia Pugliese, Luigi & Ferone, Daniele & Macrina, Giusy & Festa, Paola & Guerriero, Francesca, 2023. "The crowd-shipping with penalty cost function and uncertain travel times," Omega, Elsevier, vol. 115(C).
    6. Auad, Ramon & Erera, Alan & Savelsbergh, Martin, 2023. "Courier satisfaction in rapid delivery systems using dynamic operating regions," Omega, Elsevier, vol. 121(C).
    7. Soeffker, Ninja & Ulmer, Marlin W. & Mattfeld, Dirk C., 2022. "Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review," European Journal of Operational Research, Elsevier, vol. 298(3), pages 801-820.
    8. dos Santos, André Gustavo & Viana, Ana & Pedroso, João Pedro, 2022. "2-echelon lastmile delivery with lockers and occasional couriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    9. Cebeci, Merve Seher & Tapia, Rodrigo Javier & Kroesen, Maarten & de Bok, Michiel & Tavasszy, Lóránt, 2023. "The effect of trust on the choice for crowdshipping services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    10. Chen, Xinwei & Ulmer, Marlin W. & Thomas, Barrett W., 2022. "Deep Q-learning for same-day delivery with vehicles and drones," European Journal of Operational Research, Elsevier, vol. 298(3), pages 939-952.
    11. Ausseil, Rosemonde & Ulmer, Marlin W. & Pazour, Jennifer A., 2024. "Online acceptance probability approximation in peer-to-peer transportation," Omega, Elsevier, vol. 123(C).
    12. Nieto-Isaza, Santiago & Fontaine, Pirmin & Minner, Stefan, 2022. "The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: A Benders decomposition approach," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 62-79.
    13. Martin W.P Savelsbergh & Marlin W. Ulmer, 2022. "Challenges and opportunities in crowdsourced delivery planning and operations," 4OR, Springer, vol. 20(1), pages 1-21, March.
    14. Xiao, Haohan & Xu, Min & Wang, Shuaian, 2023. "Crowd-shipping as a Service: Game-based operating strategy design and analysis," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    15. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    16. Abdollahi, Mohammad & Yang, Xinan & Nasri, Moncef Ilies & Fairbank, Michael, 2023. "Demand management in time-slotted last-mile delivery via dynamic routing with forecast orders," European Journal of Operational Research, Elsevier, vol. 309(2), pages 704-718.
    17. Mancini, Simona & Gansterer, Margaretha, 2022. "Bundle generation for last-mile delivery with occasional drivers," Omega, Elsevier, vol. 108(C).
    18. Boysen, Nils & Emde, Simon & Schwerdfeger, Stefan, 2022. "Crowdshipping by employees of distribution centers: Optimization approaches for matching supply and demand," European Journal of Operational Research, Elsevier, vol. 296(2), pages 539-556.
    19. Ghaderi, Hadi & Zhang, Lele & Tsai, Pei-Wei & Woo, Jihoon, 2022. "Crowdsourced last-mile delivery with parcel lockers," International Journal of Production Economics, Elsevier, vol. 251(C).
    20. Tao, Jiawei & Dai, Hongyan & Chen, Weiwei & Jiang, Hai, 2023. "The value of personalized dispatch in O2O on-demand delivery services," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1022-1035.
    21. Wang, Li & Xu, Min & Qin, Hu, 2023. "Joint optimization of parcel allocation and crowd routing for crowdsourced last-mile delivery," Transportation Research Part B: Methodological, Elsevier, vol. 171(C), pages 111-135.
    22. Hou, Ting & Zhang, Wen, 2021. "Optimal two-stage elimination contests for crowdsourcing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    23. Silva, Marco & Pedroso, João Pedro & Viana, Ana, 2023. "Stochastic crowd shipping last-mile delivery with correlated marginals and probabilistic constraints," European Journal of Operational Research, Elsevier, vol. 307(1), pages 249-265.
    24. Marco Silva & João Pedro Pedroso, 2022. "Deep Reinforcement Learning for Crowdshipping Last-Mile Delivery with Endogenous Uncertainty," Mathematics, MDPI, vol. 10(20), pages 1-23, October.
    25. Zehtabian, Shohre & Larsen, Christian & Wøhlk, Sanne, 2022. "Estimation of the arrival time of deliveries by occasional drivers in a crowd-shipping setting," European Journal of Operational Research, Elsevier, vol. 303(2), pages 616-632.

    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. Ehmke, Jan Fabian & Campbell, Ann Melissa, 2014. "Customer acceptance mechanisms for home deliveries in metropolitan areas," European Journal of Operational Research, Elsevier, vol. 233(1), pages 193-207.
    2. Koch, Sebastian & Klein, Robert, 2020. "Route-based approximate dynamic programming for dynamic pricing in attended home delivery," European Journal of Operational Research, Elsevier, vol. 287(2), pages 633-652.
    3. Marlin Ulmer & Martin Savelsbergh, 2020. "Workforce Scheduling in the Era of Crowdsourced Delivery," Transportation Science, INFORMS, vol. 54(4), pages 1113-1133, July.
    4. Wang, Zheng, 2018. "Delivering meals for multiple suppliers: Exclusive or sharing logistics service," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 496-512.
    5. Cleophas, Catherine & Cottrill, Caitlin & Ehmke, Jan Fabian & Tierney, Kevin, 2019. "Collaborative urban transportation: Recent advances in theory and practice," European Journal of Operational Research, Elsevier, vol. 273(3), pages 801-816.
    6. Klapp, Mathias A. & Erera, Alan L. & Toriello, Alejandro, 2020. "Request acceptance in same-day delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    7. Lahyani, Rahma & Khemakhem, Mahdi & Semet, Frédéric, 2015. "Rich vehicle routing problems: From a taxonomy to a definition," European Journal of Operational Research, Elsevier, vol. 241(1), pages 1-14.
    8. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    9. Asbach, Lasse & Dorndorf, Ulrich & Pesch, Erwin, 2009. "Analysis, modeling and solution of the concrete delivery problem," European Journal of Operational Research, Elsevier, vol. 193(3), pages 820-835, March.
    10. Nicolas Rincon-Garcia & Ben J. Waterson & Tom J. Cherrett, 2018. "Requirements from vehicle routing software: perspectives from literature, developers and the freight industry," Transport Reviews, Taylor & Francis Journals, vol. 38(1), pages 117-138, January.
    11. Schmid, Verena & Doerner, Karl F. & Laporte, Gilbert, 2013. "Rich routing problems arising in supply chain management," European Journal of Operational Research, Elsevier, vol. 224(3), pages 435-448.
    12. Gerhard Hiermann & Matthias Prandtstetter & Andrea Rendl & Jakob Puchinger & Günther Raidl, 2015. "Metaheuristics for solving a multimodal home-healthcare scheduling problem," 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. 23(1), pages 89-113, March.
    13. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    14. Chiang, Wen-Chyuan & Russell, Robert & Xu, Xiaojing & Zepeda, David, 2009. "A simulation/metaheuristic approach to newspaper production and distribution supply chain problems," International Journal of Production Economics, Elsevier, vol. 121(2), pages 752-767, October.
    15. Ullrich, Christian A., 2013. "Integrated machine scheduling and vehicle routing with time windows," European Journal of Operational Research, Elsevier, vol. 227(1), pages 152-165.
    16. Qi, Mingyao & Lin, Wei-Hua & Li, Nan & Miao, Lixin, 2012. "A spatiotemporal partitioning approach for large-scale vehicle routing problems with time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(1), pages 248-257.
    17. Zäpfel, Günther & Bögl, Michael, 2008. "Multi-period vehicle routing and crew scheduling with outsourcing options," International Journal of Production Economics, Elsevier, vol. 113(2), pages 980-996, June.
    18. Jeffrey W. Ohlmann & Michael J. Fry & Barrett W. Thomas, 2008. "Route Design for Lean Production Systems," Transportation Science, INFORMS, vol. 42(3), pages 352-370, August.
    19. Zhiping Zuo & Yanhui Li & Jing Fu & Jianlin Wu, 2019. "Human Resource Scheduling Model and Algorithm with Time Windows and Multi-Skill Constraints," Mathematics, MDPI, vol. 7(7), pages 1-18, July.
    20. Michelle Dunbar & Simon Belieres & Nagesh Shukla & Mehrdad Amirghasemi & Pascal Perez & Nishikant Mishra, 2020. "A genetic column generation algorithm for sustainable spare part delivery: application to the Sydney DropPoint network," Annals of Operations Research, Springer, vol. 290(1), pages 923-941, July.

    More about this item

    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:bla:popmgt:v:29:y:2020:i:9:p:2153-2174. 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: Wiley Content Delivery (email available below). General contact details of provider: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1937-5956 .

    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.