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Dynamic pricing of flexible time slots for attended home delivery

Author

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  • Strauss, Arne
  • Gülpınar, Nalan
  • Zheng, Yijun

Abstract

In e-commerce, customers are usually offered a menu of home delivery time windows of which they need to select exactly one, even though at least some customers may be more flexible. To exploit the flexibility of such customers, we propose to introduce flexible delivery time slots, defined as any combination of such regular time windows (not necessarily adjacent). In selecting a flexible time slot (out of a set of windows that form the flexible product), the customer agrees to be informed only shortly prior to the dispatching of the delivery vehicle in which regular time window the goods will arrive. In return for providing this flexibility, the company may offer the customer a reduced delivery charge and/or highlight the environmental benefits. Our framework also can accommodate customized flexible slots where customers can self-select a set of regular slots in which a delivery may take place.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:294:y:2021:i:3:p:1022-1041
    DOI: 10.1016/j.ejor.2020.03.007
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    References listed on IDEAS

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    2. Zang, Xiaoning & Jiang, Li & Liang, Changyong & Fang, Xiang, 2023. "Coordinated home and locker deliveries: An exact approach for the urban delivery problem with conflicting time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    3. Zhai, Yue & Hua, Guowei & Cheng, Meng & Cheng, T.C.E., 2023. "Production lead-time hedging and order allocation in an MTO supply chain," European Journal of Operational Research, Elsevier, vol. 311(3), pages 887-905.
    4. Özarık, Sami Serkan & Lurkin, Virginie & Veelenturf, Lucas P. & Van Woensel, Tom & Laporte, Gilbert, 2023. "An Adaptive Large Neighborhood Search heuristic for last-mile deliveries under stochastic customer availability and multiple visits," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 194-220.
    5. Srinivas, Sharan & Ramachandiran, Surya & Rajendran, Suchithra, 2022. "Autonomous robot-driven deliveries: A review of recent developments and future directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    6. Jan Bruha & Hana Bruhova Foltynova, 2023. "Long-Term Impacts of the COVID-19 Pandemic on Working from Home and Online Shopping: Evidence from a Czech Panel Survey," Working Papers 2023/9, Czech National Bank.
    7. 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.
    8. Wassmuth, K. & Köhler, C. & Agatz, N.A.H. & Fleischmann, M., 2022. "Demand Management for Attended Home Delivery – A Literature Review," ERIM Report Series Research in Management ERS-2022-002-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.
    9. 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.
    10. 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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