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Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management

Author

Listed:
  • van der Hagen, L.
  • Agatz, N.A.H.
  • Spliet, R.
  • Visser, T.R.
  • Kok, A.L.
Abstract
Online grocers typically let customers choose a delivery time slot to receive their goods. To ensure a reliable service, the retailer may want to close time slots as capacity fills up. The number of customers that can be served per slot largely depends on the specific order sizes and delivery locations. Conceptually, checking whether it is possible to serve a certain customer in a certain time slot given a set of already accepted customer orders involves solving a vehicle routing problem with time windows. This is challenging in practice as there is little time available and not all relevant information is known in advance. We explore the use of machine learning to support time slot decisions in this context. Our results on realistic instances using a commercial route solver suggest that machine learning can be a promising way to assess the feasibility of customer insertions. On large-scale routing problems it performs better than insertion heuristics

Suggested Citation

  • van der Hagen, L. & Agatz, N.A.H. & Spliet, R. & Visser, T.R. & Kok, A.L., 2022. "Machine Learning-Based Feasibility Checks for Dynamic Time Slot Management," ERIM Report Series Research in Management ERS-2022-001-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.
    • Handle: RePEc:ems:eureri:137095
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    References listed on IDEAS

    as
    1. Carlos F. Daganzo, 1987. "Modeling Distribution Problems with Time Windows. Part II: Two Customer Types," Transportation Science, INFORMS, vol. 21(3), pages 180-187, August.
    2. Köhler, Charlotte & Ehmke, Jan Fabian & Campbell, Ann Melissa, 2020. "Flexible time window management for attended home deliveries," Omega, Elsevier, vol. 91(C).
    3. Robert Klein & Michael Neugebauer & Dimitri Ratkovitch & Claudius Steinhardt, 2019. "Differentiated Time Slot Pricing Under Routing Considerations in Attended Home Delivery," Service Science, INFORMS, vol. 53(1), pages 236-255, February.
    4. Catherine Cleophas & Jan Ehmke, 2014. "When Are Deliveries Profitable?," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(3), pages 153-163, June.
    5. Ann Melissa Campbell & Martin W. P. Savelsbergh, 2005. "Decision Support for Consumer Direct Grocery Initiatives," Transportation Science, INFORMS, vol. 39(3), pages 313-327, August.
    6. Robert Klein & Jochen Mackert & Michael Neugebauer & Claudius Steinhardt, 2018. "A model-based approximation of opportunity cost for dynamic pricing in attended home delivery," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 969-996, October.
    7. Carlos F. Daganzo, 1987. "Modeling Distribution Problems with Time Windows: Part I," Transportation Science, INFORMS, vol. 21(3), pages 171-179, August.
    8. Strauss, Arne & Gülpınar, Nalan & Zheng, Yijun, 2021. "Dynamic pricing of flexible time slots for attended home delivery," European Journal of Operational Research, Elsevier, vol. 294(3), pages 1022-1041.
    9. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    10. Xinan Yang & Arne K. Strauss & Christine S. M. Currie & Richard Eglese, 2016. "Choice-Based Demand Management and Vehicle Routing in E-Fulfillment," Transportation Science, INFORMS, vol. 50(2), pages 473-488, May.
    11. Niels Agatz & Ann Campbell & Moritz Fleischmann & Martin Savelsbergh, 2011. "Time Slot Management in Attended Home Delivery," Transportation Science, INFORMS, vol. 45(3), pages 435-449, August.
    12. Ann Melissa Campbell & Martin Savelsbergh, 2006. "Incentive Schemes for Attended Home Delivery Services," Transportation Science, INFORMS, vol. 40(3), pages 327-341, August.
    13. Thomas R. Visser & Remy Spliet, 2020. "Efficient Move Evaluations for Time-Dependent Vehicle Routing Problems," Transportation Science, INFORMS, vol. 54(4), pages 1091-1112, July.
    14. Yang, Xinan & Strauss, Arne K., 2017. "An approximate dynamic programming approach to attended home delivery management," European Journal of Operational Research, Elsevier, vol. 263(3), pages 935-945.
    15. Visser, T.R. & Agatz, N.A.H. & Spliet, R., 2019. "Simultaneous customer interaction in online booking systems for attended home delivery," ERIM Report Series Research in Management ERS-2019-011-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.
    16. Ehmke, Jan Fabian & Campbell, Ann Melissa & Urban, Timothy L., 2015. "Ensuring service levels in routing problems with time windows and stochastic travel times," European Journal of Operational Research, Elsevier, vol. 240(2), pages 539-550.
    17. 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.
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    Cited by:

    1. Waßmuth, Katrin & Köhler, Charlotte & Agatz, Niels & Fleischmann, Moritz, 2023. "Demand management for attended home delivery—A literature review," European Journal of Operational Research, Elsevier, vol. 311(3), pages 801-815.
    2. 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.

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    More about this item

    Keywords

    time slot management; vehicle routing; supervised machine learning;
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