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Time Slot Management in Attended Home Delivery

Author

Listed:
  • Niels Agatz

    (Department of Decision and Information Science, Rotterdam School of Management, Erasmus Univeristy, 3062 PA DR Rotterdam, The Netherlands)

  • Ann Campbell

    (Department of Management Sciences, Tipple College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Moritz Fleischmann

    (Logistics and Supply Chain Management, University of Mannheim Business School, 68131 Mannheim, Germany)

  • Martin Savelsbergh

    (CISRO Mathematics, Informatics, and Statistics, North Ryde, NSW 1670 Australia)

Abstract

Many e-tailers providing attended home delivery, especially e-grocers, offer narrow delivery time slots to ensure satisfactory customer service. The choice of delivery time slots has to balance marketing and operational considerations, which results in a complex planning problem. We study the problem of selecting the set of time slots to offer in each of the zip codes in a service region. The selection needs to facilitate cost-effective delivery routes, but also needs to ensure an acceptable level of service to the customer. We present a fully automated approach that is capable of producing high-quality delivery time slot offerings in a short amount of time. Computational experiments reveal the value of this approach and the impact of the environment on the underlying trade-offs.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ortrsc:v:45:y:2011:i:3:p:435-449
    DOI: 10.1287/trsc.1100.0346
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    e-grocery; home delivery; time slots; vehicle routing; continuous approximation; integer programming;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics

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