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The Restaurant Meal Delivery Problem: Dynamic Pickup and Delivery with Deadlines and Random Ready Times

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  • Marlin W. Ulmer

    (Carl-Friedrich-Gauß-Fakultät, Technische Universität Braunschweig, Braunschweig, Germany 38106;)

  • Barrett W. Thomas

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Ann Melissa Campbell

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Nicholas Woyak

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

Abstract

We consider a stochastic dynamic pickup and delivery problem in which a fleet of drivers delivers food from a set of restaurants to ordering customers. The objective is to dynamically control a fleet of drivers in a way that avoids delays with respect to customers’ deadlines. There are two sources of uncertainty in the problem. First, the customers are unknown until they place an order. Second, the time at which the food is ready at the restaurant is unknown. To address these challenges, we present an anticipatory customer assignment (ACA) policy. To account for the stochasticity in the problem, ACA postpones the assignment decisions for selected customers, allowing more flexibility in assignments. In addition, ACA introduces a time buffer to reduce making decisions that are likely to result in delays. We also consider bundling, which is the practice of assigning multiple orders at a time to a driver. Based on real-world data, we show how ACA is able to improve service significantly for all stakeholders compared with current practice.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:55:y:2021:i:1:p:75-100
    DOI: 10.1287/trsc.2020.1000
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    References listed on IDEAS

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    Cited by:

    1. Bosse, Alexander & Ulmer, Marlin W. & Manni, Emanuele & Mattfeld, Dirk C., 2023. "Dynamic priority rules for combining on-demand passenger transportation and transportation of goods," European Journal of Operational Research, Elsevier, vol. 309(1), pages 399-408.
    2. Côté, Jean-François & Alves de Queiroz, Thiago & Gallesi, Francesco & Iori, Manuel, 2023. "A branch-and-regret algorithm for the same-day delivery problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    3. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    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. 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.
    6. Marlin W. Ulmer & Alan Erera & Martin Savelsbergh, 2022. "Dynamic service area sizing in urban delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 763-793, September.
    7. Auad, Ramon & Erera, Alan & Savelsbergh, Martin, 2023. "Courier satisfaction in rapid delivery systems using dynamic operating regions," Omega, Elsevier, vol. 121(C).
    8. Liu, Yang & Li, Sen, 2023. "An economic analysis of on-demand food delivery platforms: Impacts of regulations and integration with ride-sourcing platforms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    9. Du, Zhong & Fan, Zhi-Ping & Chen, Zhongwei, 2023. "Implications of on-time delivery service with compensation for an online food delivery platform and a restaurant," International Journal of Production Economics, Elsevier, vol. 262(C).
    10. Long He & Sheng Liu & Zuo‐Jun Max Shen, 2022. "Smart urban transport and logistics: A business analytics perspective," Production and Operations Management, Production and Operations Management Society, vol. 31(10), pages 3771-3787, October.
    11. 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.
    12. Banerjee, Dipayan & Erera, Alan L. & Stroh, Alexander M. & Toriello, Alejandro, 2023. "Who has access to e-commerce and when? Time-varying service regions in same-day delivery," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 148-168.
    13. Zhan, Xingbin & Szeto, W.Y. & Wang, Yue, 2023. "The ride-hailing sharing problem with parcel transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 172(C).
    14. 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.
    15. Lin, Meiyan & Lin, Shaodan & Ma, Lijun & Zhang, Lianmin, 2022. "The value of the Physical Internet on the meals-on-wheels delivery system," International Journal of Production Economics, Elsevier, vol. 248(C).

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