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Improving fleet utilization for carriers by interval scheduling


  • Lee, Soonhui
  • Turner, Jonathan
  • Daskin, Mark S.
  • Homem-de-Mello, Tito
  • Smilowitz, Karen


Carriers are under increasing pressure to offset rising fuel charges with cost cutting or revenue generating schemes. One opportunity for cost reduction lies in asset management. This paper presents resource allocation scheduling models that can be used to assign truck loads to delivery times and trucks when delivery times are flexible. The paper makes two main contributions. First, we formulate the problem as a multi-objective optimization model — minimizing the number of trucks needed as well as the costs associated with tardiness or earliness — and demonstrate how improvements in fleet usage translate into savings which carriers can use as incentives to promote flexible delivery times for customers. Second, we show that a two-phase model with a polynomial algorithm in the second phase is able to produce optimal schedules in a reasonable time.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:218:y:2012:i:1:p:261-269 DOI: 10.1016/j.ejor.2011.10.019

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    References listed on IDEAS

    1. Kovalyov, Mikhail Y. & Ng, C.T. & Cheng, T.C. Edwin, 2007. "Fixed interval scheduling: Models, applications, computational complexity and algorithms," European Journal of Operational Research, Elsevier, vol. 178(2), pages 331-342, April.
    2. Daniels, Richard L., 1990. "A multi-objective approach to resource allocation in single machine scheduling," European Journal of Operational Research, Elsevier, vol. 48(2), pages 226-241, September.
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    Cited by:

    1. Janiak, Adam & Janiak, Władysław A. & Krysiak, Tomasz & Kwiatkowski, Tomasz, 2015. "A survey on scheduling problems with due windows," European Journal of Operational Research, Elsevier, vol. 242(2), pages 347-357.
    2. 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.
    3. Shi, Yuhui & Reich, Daniel & Epelman, Marina & Klampfl, Erica & Cohn, Amy, 2017. "An analytical approach to prototype vehicle test scheduling," Omega, Elsevier, vol. 67(C), pages 168-176.


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