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A problem generator-solver heuristic for vehicle routing with soft time windows

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  • Ioannou, George
  • Kritikos, Manolis
  • Prastacos, Gregory

Abstract

In this paper we consider the vehicle routing problem with soft time window constraints (VRPSTW), in which vehicles are allowed to service customers before and after the earliest and latest time window bounds, respectively. This relaxation comes at the expense of appropriate penalties that reflect the effect that time window violations have on the customers' satisfaction. The problem is of particular importance for fleet planning as it allows decision-makers from both the logistics and marketing-sales side to determine minimal fleet sizes by appropriate contract negotiations for order delivery times. To solve the problem, we couple the nearest-neighbour method with a problem generator that provides, in a structured manner, numerous instances that result from the manipulation of the level of time window violations and the population of customers that allow such violations. The proposed heuristic results in solutions that reduce the number of vehicles required for the hard case and provide minimal violations of time windows. Computational results on a set of benchmark problems show that our method outperforms previous approaches to the vehicle routing problem with soft time windows, and that it can serve as the basis for efficient and effective fleet planning.

Suggested Citation

  • Ioannou, George & Kritikos, Manolis & Prastacos, Gregory, 2003. "A problem generator-solver heuristic for vehicle routing with soft time windows," Omega, Elsevier, vol. 31(1), pages 41-53, February.
    • Handle: RePEc:eee:jomega:v:31:y:2003:i:1:p:41-53
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    References listed on IDEAS

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    1. G Ioannou & M Kritikos & G Prastacos, 2001. "A greedy look-ahead heuristic for the vehicle routing problem with time windows," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(5), pages 523-537, May.
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    5. Yiannis A. Koskosidis & Warren B. Powell & Marius M. Solomon, 1992. "An Optimization-Based Heuristic for Vehicle Routing and Scheduling with Soft Time Window Constraints," Transportation Science, INFORMS, vol. 26(2), pages 69-85, May.
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    Cited by:

    1. Zhaowei Miao & Feng Yang & Ke Fu & Dongsheng Xu, 2012. "Transshipment service through crossdocks with both soft and hard time windows," Annals of Operations Research, Springer, vol. 192(1), pages 21-47, January.
    2. 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.
    3. Bhusiri, Narath & Qureshi, Ali Gul & Taniguchi, Eiichi, 2014. "The trade-off between fixed vehicle costs and time-dependent arrival penalties in a routing problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 1-22.
    4. Sébastien Mouthuy & Florence Massen & Yves Deville & Pascal Van Hentenryck, 2015. "A Multistage Very Large-Scale Neighborhood Search for the Vehicle Routing Problem with Soft Time Windows," Transportation Science, INFORMS, vol. 49(2), pages 223-238, May.
    5. Matteo Salani & Maria Battarra, 2018. "The opportunity cost of time window violations," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(4), pages 343-361, December.
    6. Martins, Sara & Ostermeier, Manuel & Amorim, Pedro & Hübner, Alexander & Almada-Lobo, Bernardo, 2019. "Product-oriented time window assignment for a multi-compartment vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 276(3), pages 893-909.
    7. Lin, Yen-Hung & Batta, Rajan & Rogerson, Peter A. & Blatt, Alan & Flanigan, Marie, 2011. "A logistics model for emergency supply of critical items in the aftermath of a disaster," Socio-Economic Planning Sciences, Elsevier, vol. 45(4), pages 132-145, December.
    8. K H Kim & M J Lee, 2007. "Scheduling trucks in local depots for door-to-door delivery services," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(9), pages 1195-1202, September.
    9. Ciancio, Claudio & Laganá, Demetrio & Vocaturo, Francesca, 2018. "Branch-price-and-cut for the Mixed Capacitated General Routing Problem with Time Windows," European Journal of Operational Research, Elsevier, vol. 267(1), pages 187-199.
    10. Ma, Hong & Cheang, Brenda & Lim, Andrew & Zhang, Lei & Zhu, Yi, 2012. "An investigation into the vehicle routing problem with time windows and link capacity constraints," Omega, Elsevier, vol. 40(3), pages 336-347.
    11. Day, Jamison M. & Daniel Wright, P. & Schoenherr, Tobias & Venkataramanan, Munirpallam & Gaudette, Kevin, 2009. "Improving routing and scheduling decisions at a distributor of industrial gasses," Omega, Elsevier, vol. 37(1), pages 227-237, February.
    12. Yu, Min-Chun & Goh, Mark & Lin, Hung-Chung, 2012. "Fuzzy multi-objective vendor selection under lean procurement," European Journal of Operational Research, Elsevier, vol. 219(2), pages 305-311.
    13. Tang, Lixin & Wang, Xianpeng, 2009. "Simultaneously scheduling multiple turns for steel color-coating production," European Journal of Operational Research, Elsevier, vol. 198(3), pages 715-725, November.
    14. P. Kabcome & T. Mouktonglang, 2015. "Vehicle Routing Problem for Multiple Product Types, Compartments, and Trips with Soft Time Windows," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2015, pages 1-9, July.

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