IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v123y2024ics0305048323001573.html
   My bibliography  Save this article

Online acceptance probability approximation in peer-to-peer transportation

Author

Listed:
  • Ausseil, Rosemonde
  • Ulmer, Marlin W.
  • Pazour, Jennifer A.

Abstract

Crowdsourced transportation by independent suppliers (or drivers) is central to urban delivery and mobility platforms. While utilizing crowdsourced resources has several advantages, it comes with the challenge that suppliers are not bound to assignments made by the platforms. In practice, suppliers often decline offered service requests, e.g., due to the required travel detour, the expected tip, or the area a request is located. This leads to inconveniences for the platform (ineffective assignments), the corresponding customer (delayed service), and also the suppliers themselves (non-fitting assignment, less revenue). Therefore, the objective of this work is to analyze the impact of a platform approximating and incorporating individual suppliers’ acceptance behavior into the order dispatching process and to quantify its impact on all stakeholders (platform, customers, suppliers). To this end, we propose a dynamic matching problem where suppliers’ acceptances or rejections of offers are uncertain. Suppliers who accept an offered request are assigned and reenter the system after service looking for another offer. Suppliers declining an offer stay idle to wait for another offer, but leave after a limited time if no acceptable offer is made. Every supplier decision reveals only their acceptance or rejection information to the platform, and in this paper, we present a corresponding mathematical model and an approximation method that translates supplier responses into updated approximations of the likelihood of a specific supplier to accept a specific future offer and use this information to optimize subsequent offering decisions. We show via a computational study based on crowdsourced food delivery that online approximation and incorporating individual supplier acceptance estimates into order dispatching leads to overall more successful assignments, more revenue for the platform and most of the suppliers, and less waiting for the customers to be served. We also show that considering individual supplier behavior can lead to unfair treatment of more agreeable suppliers.

Suggested Citation

  • Ausseil, Rosemonde & Ulmer, Marlin W. & Pazour, Jennifer A., 2024. "Online acceptance probability approximation in peer-to-peer transportation," Omega, Elsevier, vol. 123(C).
    • Handle: RePEc:eee:jomega:v:123:y:2024:i:c:s0305048323001573
    DOI: 10.1016/j.omega.2023.102993
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048323001573
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2023.102993?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. Mourad, Abood & Puchinger, Jakob & Chu, Chengbin, 2019. "A survey of models and algorithms for optimizing shared mobility," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 323-346.
    2. Behrend, Moritz & Meisel, Frank, 2018. "The integration of item-sharing and crowdshipping: Can collaborative consumption be pushed by delivering through the crowd?," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 227-243.
    3. Qiaochu Wang & Yan Huang & Stefanus Jasin & Param Vir Singh, 2023. "Algorithmic Transparency with Strategic Users," Management Science, INFORMS, vol. 69(4), pages 2297-2317, April.
    4. Silviya Valeva & Mike Hewitt & Barrett W. Thomas, 2017. "A matheuristic for workforce planning with employee learning and stochastic demand," International Journal of Production Research, Taylor & Francis Journals, vol. 55(24), pages 7380-7397, December.
    5. Hu, Xinru & Zhou, Shuiyin & Luo, Xiaomeng & Li, Jianbin & Zhang, Chi, 2024. "Optimal pricing strategy of an on-demand platform with cross-regional passengers," Omega, Elsevier, vol. 122(C).
    6. 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.
    7. 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.
    8. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2021. "Crowdsourced delivery: A review of platforms and academic literature," Omega, Elsevier, vol. 98(C).
    9. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2024. "Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare," Omega, Elsevier, vol. 122(C).
    10. Bakker, Steffen J. & Wang, Akang & Gounaris, Chrysanthos E., 2021. "Vehicle routing with endogenous learning: Application to offshore plug and abandonment campaign planning," European Journal of Operational Research, Elsevier, vol. 289(1), pages 93-106.
    11. Yu, Guodong & Liu, Aijun & Zhang, Jianghua & Sun, Huiping, 2021. "Optimal operations planning of electric autonomous vehicles via asynchronous learning in ride-hailing systems," Omega, Elsevier, vol. 103(C).
    12. 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).
    13. Yan, Pengyu & Lee, Chung-Yee & Chu, Chengbin & Chen, Cynthia & Luo, Zhiqin, 2021. "Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability," Omega, Elsevier, vol. 102(C).
    14. Amirmahdi Tafreshian & Neda Masoud & Yafeng Yin, 2020. "Frontiers in Service Science: Ride Matching for Peer-to-Peer Ride Sharing: A Review and Future Directions," Service Science, INFORMS, vol. 12(2-3), pages 44-60, June.
    15. Horner, Hannah & Pazour, Jennifer & Mitchell, John E., 2021. "Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    16. 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.
    17. Chiwei Yan & Helin Zhu & Nikita Korolko & Dawn Woodard, 2020. "Dynamic pricing and matching in ride‐hailing platforms," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(8), pages 705-724, December.
    18. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2019. "An exact solution method for the capacitated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 279(2), pages 589-604.
    19. Auad, Ramon & Erera, Alan & Savelsbergh, Martin, 2023. "Courier satisfaction in rapid delivery systems using dynamic operating regions," Omega, Elsevier, vol. 121(C).
    20. Furuhata, Masabumi & Dessouky, Maged & Ordóñez, Fernando & Brunet, Marc-Etienne & Wang, Xiaoqing & Koenig, Sven, 2013. "Ridesharing: The state-of-the-art and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 28-46.
    21. Mancini, Simona & Gansterer, Margaretha, 2022. "Bundle generation for last-mile delivery with occasional drivers," Omega, Elsevier, vol. 108(C).
    22. Mila Nambiar & David Simchi-Levi & He Wang, 2019. "Dynamic Learning and Pricing with Model Misspecification," Management Science, INFORMS, vol. 65(11), pages 4980-5000, November.
    23. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    24. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
    25. Francisco Castro & Hamid Nazerzadeh & Chiwei Yan, 2020. "Matching queues with reneging: a product form solution," Queueing Systems: Theory and Applications, Springer, vol. 96(3), pages 359-385, December.
    26. Marlin Ulmer & Martin Savelsbergh, 2020. "Workforce Scheduling in the Era of Crowdsourced Delivery," Transportation Science, INFORMS, vol. 54(4), pages 1113-1133, July.
    27. Mofidi, Seyed Shahab & Pazour, Jennifer A., 2019. "When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 1-23.
    28. He, Bo & Gupta, Varun & Mirchandani, Prakash, 2021. "Online selling through O2O platform or on your own? Strategic implications for local Brick-and-Mortar stores," Omega, Elsevier, vol. 103(C).
    29. Xi Chen & Jianjun Gao & Dongdong Ge & Zizhuo Wang, 2022. "Bayesian dynamic learning and pricing with strategic customers," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3125-3142, August.
    30. 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.
    31. Junyu Cao & Mariana Olvera-Cravioto & Zuo-Jun (Max) Shen, 2020. "Last-Mile Shared Delivery: A Discrete Sequential Packing Approach," Mathematics of Operations Research, INFORMS, vol. 45(4), pages 1466-1497, November.
    32. Cody Cook & Rebecca Diamond & Jonathan V Hall & John A List & Paul Oyer, 2021. "The Gender Earnings Gap in the Gig Economy: Evidence from over a Million Rideshare Drivers [Measuring the Gig Economy: Current Knowledge and Open Issues]," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 88(5), pages 2210-2238.
    33. Wang, Hai & Yang, Hai, 2019. "Ridesourcing systems: A framework and review," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 122-155.
    34. Ulmer, Marlin & Nowak, Maciek & Mattfeld, Dirk & Kaminski, Bogumił, 2020. "Binary driver-customer familiarity in service routing," European Journal of Operational Research, Elsevier, vol. 286(2), pages 477-493.
    35. Xinyu Cao & Juanjuan Zhang, 2021. "Preference Learning and Demand Forecast," Marketing Science, INFORMS, vol. 40(1), pages 62-79, January.
    36. Marlin W. Ulmer & Dirk C. Mattfeld & Felix Köster, 2018. "Budgeting Time for Dynamic Vehicle Routing with Stochastic Customer Requests," Transportation Science, INFORMS, vol. 52(1), pages 20-37, January.
    37. 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.
    38. Omar Besbes & Assaf Zeevi, 2009. "Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms," Operations Research, INFORMS, vol. 57(6), pages 1407-1420, December.
    39. 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.
    40. Huan Jin & Mike Hewitt & Barrett W. Thomas, 2018. "Workforce grouping and assignment with learning-by-doing and knowledge transfer," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4968-4982, July.
    41. 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.
    42. Yixiao Huang & Lei Zhao & Warren B. Powell & Yue Tong & Ilya O. Ryzhov, 2019. "Optimal Learning for Urban Delivery Fleet Allocation," Transportation Science, INFORMS, vol. 53(3), pages 623-641, May.
    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. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2024. "Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare," Omega, Elsevier, vol. 122(C).
    2. Yang, Dingtong & Hyland, Michael F. & Jayakrishnan, R., 2024. "Tackling the crowdsourced shared-trip delivery problem at scale with a novel decomposition heuristic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 188(C).
    3. 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.
    4. 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.
    5. Mancini, Simona & Gansterer, Margaretha, 2022. "Bundle generation for last-mile delivery with occasional drivers," Omega, Elsevier, vol. 108(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. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    8. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2021. "A multi-period analysis of the integrated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 292(2), pages 483-499.
    9. 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.
    10. Horner, Hannah & Pazour, Jennifer & Mitchell, John E., 2021. "Optimizing driver menus under stochastic selection behavior for ridesharing and crowdsourced delivery," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    11. Behrend, Moritz & Meisel, Frank & Fagerholt, Kjetil & Andersson, Henrik, 2019. "An exact solution method for the capacitated item-sharing and crowdshipping problem," European Journal of Operational Research, Elsevier, vol. 279(2), pages 589-604.
    12. Marlin Ulmer & Martin Savelsbergh, 2020. "Workforce Scheduling in the Era of Crowdsourced Delivery," Transportation Science, INFORMS, vol. 54(4), pages 1113-1133, July.
    13. 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.
    14. Stokkink, Patrick & Cordeau, Jean-François & Geroliminis, Nikolas, 2024. "A column and row generation approach to the crowd-shipping problem with transfers," Omega, Elsevier, vol. 128(C).
    15. Mofidi, Seyed Shahab & Pazour, Jennifer A., 2019. "When is it beneficial to provide freelance suppliers with choice? A hierarchical approach for peer-to-peer logistics platforms," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 1-23.
    16. Nie, Fapeng & Li, Xiang & Zhou, Chang, 2024. "Impact of privacy regulation involving information collection on the ride-hailing market," Omega, Elsevier, vol. 126(C).
    17. Hu, Xinru & Zhou, Shuiyin & Luo, Xiaomeng & Li, Jianbin & Zhang, Chi, 2024. "Optimal pricing strategy of an on-demand platform with cross-regional passengers," Omega, Elsevier, vol. 122(C).
    18. Fan, Wenbo & Gu, Weihua & Xu, Meng, 2024. "Optimal design of ride-pooling as on-demand feeder services," Transportation Research Part B: Methodological, Elsevier, vol. 185(C).
    19. Li, Yuanyuan & Liu, Yang, 2021. "Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    20. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.

    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:eee:jomega:v:123:y:2024:i:c:s0305048323001573. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.