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The Dynamic Dispatch Waves Problem for same-day delivery

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  • Klapp, Mathias A.
  • Erera, Alan L.
  • Toriello, Alejandro
Abstract
We study same-day delivery (SDD) systems by formulating the Dynamic Dispatch Waves Problem (DDWP). The DDWP models an order dispatching problem faced by a distribution center, where orders arise dynamically throughout a service day and must be delivered by day’s end. At each decision epoch (wave), the system’s operator chooses whether or not to dispatch a single vehicle loaded with orders ready for service in order to minimize vehicle travel costs and penalties for unserved requests. We formulate an arc-based integer programming model and design local search heuristics to solve a deterministic DDWP where order arrival times are known in advance. We use the deterministic variant to design an a priori solution approach, and provide two approaches to obtain dynamic policies using the a priori solution. We test and compare solution approaches on two sets of instances with different geography scenarios, size, information dynamism, and order timing variability. The computational results suggest that our best dynamic policy can reduce the average cost of an a priori policy by 9.1% and substantially improves the fraction of orders delivered (order coverage), demonstrating the importance of reactive optimization for dynamic SDD services. We also analyze the tradeoff between two common SDD objectives: total cost minimization versus order coverage maximization. We find structural differences in the dispatch frequency and route duration of solutions for the two different objectives, and demonstrate empirically that small increases in order coverage may require substantial increases in vehicle travel cost.

Suggested Citation

  • Klapp, Mathias A. & Erera, Alan L. & Toriello, Alejandro, 2018. "The Dynamic Dispatch Waves Problem for same-day delivery," European Journal of Operational Research, Elsevier, vol. 271(2), pages 519-534.
    • Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:519-534
    DOI: 10.1016/j.ejor.2018.05.032
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    5. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
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    7. 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.
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    9. Zhong, Shuya & Giannikas, Vaggelis & Merino, Jorge & McFarlane, Duncan & Cheng, Jun & Shao, Wei, 2022. "Evaluating the benefits of picking and packing planning integration in e-commerce warehouses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 67-81.
    10. Kim, Nayeon & Montreuil, Benoit & Klibi, Walid & Zied Babai, M., 2023. "Network inventory deployment for responsive fulfillment," International Journal of Production Economics, Elsevier, vol. 255(C).
    11. Liu, Zeyu & Li, Xueping & Khojandi, Anahita, 2022. "The flying sidekick traveling salesman problem with stochastic travel time: A reinforcement learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
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    13. 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.
    14. 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).
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    18. Rasini, Monica & Agatz, Niels & Tappia, Elena, 2020. "Anticipatory shipment for pickup point supply," Omega, Elsevier, vol. 93(C).
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    20. Klein, Vienna & Steinhardt, Claudius, 2023. "Dynamic demand management and online tour planning for same-day delivery," European Journal of Operational Research, Elsevier, vol. 307(2), pages 860-886.
    21. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    22. Arslan, A.M. & Agatz, N.A.H. & Klapp, M., 2019. "Operational Strategies for On-demand Personal Shopper Services," ERIM Report Series Research in Management ERS-2019-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    23. Soares, Gabriel & Bulhões, Teobaldo & Bruck, Bruno, 2024. "An efficient hybrid genetic algorithm for the traveling salesman problem with release dates," European Journal of Operational Research, Elsevier, vol. 318(1), pages 31-42.

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