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Cyclic-order neighborhoods with application to the vehicle routing problem with stochastic demand

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  • Goodson, Justin C.
  • Ohlmann, Jeffrey W.
  • Thomas, Barrett W.
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    Abstract

    We examine neighborhood structures for heuristic search applicable to a general class of vehicle routing problems (VRPs). Our methodology utilizes a cyclic-order solution encoding, which maps a permutation of the customer set to a collection of many possible VRP solutions. We identify the best VRP solution in this collection via a polynomial-time algorithm from the literature. We design neighborhoods to search the space of cyclic orders. Utilizing a simulated annealing framework, we demonstrate the potential of cyclic-order neighborhoods to facilitate the discovery of high quality a priori solutions for the vehicle routing problem with stochastic demand (VRPSD). Without tailoring our solution procedure to this specific routing problem, we are able to match 16 of 19 known optimal VRPSD solutions. We also propose an updating procedure to evaluate the neighbors of a current solution and demonstrate its ability to reduce the computational expense of our approach.

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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 217 (2012)
    Issue (Month): 2 ()
    Pages: 312-323

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    Handle: RePEc:eee:ejores:v:217:y:2012:i:2:p:312-323

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    Web page: http://www.elsevier.com/locate/eor

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    Keywords: Heuristics; Logistics; Cyclic-order neighborhoods; Stochastic vehicle routing; Simulated annealing;

    References

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    1. Renaud, Jacques & Boctor, Fayez F., 2002. "A sweep-based algorithm for the fleet size and mix vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 140(3), pages 618-628, August.
    2. Armentano, Vinicius Amaral & de Franca Filho, Moacir Felizardo, 2007. "Minimizing total tardiness in parallel machine scheduling with setup times: An adaptive memory-based GRASP approach," European Journal of Operational Research, Elsevier, vol. 183(1), pages 100-114, November.
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    Cited by:
    1. Shukla, Nagesh & Choudhary, A.K. & Prakash, P.K.S. & Fernandes, K.J. & Tiwari, M.K., 2013. "Algorithm portfolios for logistics optimization considering stochastic demands and mobility allowance," International Journal of Production Economics, Elsevier, vol. 141(1), pages 146-166.

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