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Vehicle routing with soft time windows and Erlang travel times

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  • R A Russell

    (The University of Tulsa)

  • T L Urban

    (The University of Tulsa)

Abstract

This paper investigates vehicle-routing problems in which the travel times are random variables, and deliveries are made subject to soft time-window constraints. In particular, we model the travel time using a shifted gamma distribution. Penalties are incurred for deviations from the customers' time windows—early or late—and are developed using a fixed cost, a linear cost penalty, and/or a quadratic loss penalty. Alternatively, specifying a given probability of meeting the time-window constraints is considered. A tabu-search metaheuristic is developed, and computational results on test problems from the literature are reported.

Suggested Citation

  • R A Russell & T L Urban, 2008. "Vehicle routing with soft time windows and Erlang travel times," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1220-1228, September.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:9:d:10.1057_palgrave.jors.2602465
    DOI: 10.1057/palgrave.jors.2602465
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    References listed on IDEAS

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    8. Junlong Zhang & William Lam & Bi Chen, 2013. "A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service," Networks and Spatial Economics, Springer, vol. 13(4), pages 471-496, December.
    9. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
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    19. Taş, D. & Gendreau, M. & Dellaert, N. & van Woensel, T. & de Kok, A.G., 2014. "Vehicle routing with soft time windows and stochastic travel times: A column generation and branch-and-price solution approach," European Journal of Operational Research, Elsevier, vol. 236(3), pages 789-799.
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