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Dynamic fleet scheduling with uncertain demand and customer flexibility

Author

Listed:
  • Jonathan Turner
  • Soonhui Lee
  • Mark Daskin
  • Tito Homem-de-Mello
  • Karen Smilowitz

Abstract

We develop a dynamic fleet scheduling model that demonstrates how a carrier can improve fleet utilization. The fleet scheduling model presented by Lee et al. (Eur J Oper Res 218(1):261–269, 2012 ) minimizes (1) a carrier’s fleet size and (2) the penalty associated with the alternative delivery times selected. The model is static since requests are collected over time and processed together. In this paper we present a stochastic, dynamic version of the fleet reduction model. As demand is revealed throughout an order horizon, decisions are made in stages by sampling anticipated demand to avoid recourse penalties in later stages. Based on computational experiments we find the following: 1. Modeling stochasticity improves the quality of solutions relative to the analogous model that does not include stochasticity. Counter-intuitively, an order lead-time distribution in which most loads are requested early can negatively impact optimal solution costs. 2. The stochastic model produces good results without requiring prohibitively large numbers of demand scenarios. 3. Consignees that place orders early in the order horizon are more often assigned their requested delivery times than those who place orders late. Copyright Springer-Verlag 2012

Suggested Citation

  • Jonathan Turner & Soonhui Lee & Mark Daskin & Tito Homem-de-Mello & Karen Smilowitz, 2012. "Dynamic fleet scheduling with uncertain demand and customer flexibility," Computational Management Science, Springer, vol. 9(4), pages 459-481, November.
    • Handle: RePEc:spr:comgts:v:9:y:2012:i:4:p:459-481
    DOI: 10.1007/s10287-012-0145-3
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    References listed on IDEAS

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    1. Ilya Gertsbakh & Helman I. Stern, 1978. "Minimal Resources for Fixed and Variable Job Schedules," Operations Research, INFORMS, vol. 26(1), pages 68-85, February.
    2. Emmons, Hamilton & Pinedo, Michael, 1990. "Scheduling stochastic jobs with due dates on parallel machines," European Journal of Operational Research, Elsevier, vol. 47(1), pages 49-55, July.
    3. Ann Melissa Campbell & Martin Savelsbergh, 2006. "Incentive Schemes for Attended Home Delivery Services," Transportation Science, INFORMS, vol. 40(3), pages 327-341, August.
    4. Nicole Megow & Marc Uetz & Tjark Vredeveld, 2006. "Models and Algorithms for Stochastic Online Scheduling," Mathematics of Operations Research, INFORMS, vol. 31(3), pages 513-525, August.
    5. Michael Z. Spivey & Warren B. Powell, 2004. "The Dynamic Assignment Problem," Transportation Science, INFORMS, vol. 38(4), pages 399-419, November.
    6. Lee, Soonhui & Turner, Jonathan & Daskin, Mark S. & Homem-de-Mello, Tito & Smilowitz, Karen, 2012. "Improving fleet utilization for carriers by interval scheduling," European Journal of Operational Research, Elsevier, vol. 218(1), pages 261-269.
    7. Gregory A. Godfrey & Warren B. Powell, 2002. "An Adaptive Dynamic Programming Algorithm for Dynamic Fleet Management, I: Single Period Travel Times," Transportation Science, INFORMS, vol. 36(1), pages 21-39, February.
    8. List, George F. & Wood, Bryan & Nozick, Linda K. & Turnquist, Mark A. & Jones, Dean A. & Kjeldgaard, Edwin A. & Lawton, Craig R., 2003. "Robust optimization for fleet planning under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 39(3), pages 209-227, May.
    9. Astrid S. Kenyon & David P. Morton, 2003. "Stochastic Vehicle Routing with Random Travel Times," Transportation Science, INFORMS, vol. 37(1), pages 69-82, February.
    10. Barry C. Smith & Ellis L. Johnson, 2006. "Robust Airline Fleet Assignment: Imposing Station Purity Using Station Decomposition," Transportation Science, INFORMS, vol. 40(4), pages 497-516, November.
    11. Michael Pinedo, 1983. "Stochastic Scheduling with Release Dates and Due Dates," Operations Research, INFORMS, vol. 31(3), pages 559-572, June.
    12. Soumia Ichoua & Michel Gendreau & Jean-Yves Potvin, 2006. "Exploiting Knowledge About Future Demands for Real-Time Vehicle Dispatching," Transportation Science, INFORMS, vol. 40(2), pages 211-225, May.
    13. Jeff Linderoth & Alexander Shapiro & Stephen Wright, 2006. "The empirical behavior of sampling methods for stochastic programming," Annals of Operations Research, Springer, vol. 142(1), pages 215-241, February.
    14. Felix Papier & Ulrich W. Thonemann, 2008. "Queuing Models for Sizing and Structuring Rental Fleets," Transportation Science, INFORMS, vol. 42(3), pages 302-317, August.
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