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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.

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.

Suggested Citation

  • Goodson, Justin C. & Ohlmann, Jeffrey W. & Thomas, Barrett W., 2012. "Cyclic-order neighborhoods with application to the vehicle routing problem with stochastic demand," European Journal of Operational Research, Elsevier, vol. 217(2), pages 312-323.
    • Handle: RePEc:eee:ejores:v:217:y:2012:i:2:p:312-323
    DOI: 10.1016/j.ejor.2011.09.023
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    Cited by:

    1. Luo, Zhixing & Qin, Hu & Zhang, Dezhi & Lim, Andrew, 2016. "Adaptive large neighborhood search heuristics for the vehicle routing problem with stochastic demands and weight-related cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 85(C), pages 69-89.
    2. Bertazzi, Luca & Secomandi, Nicola, 2018. "Faster rollout search for the vehicle routing problem with stochastic demands and restocking," European Journal of Operational Research, Elsevier, vol. 270(2), pages 487-497.
    3. Florent Hernandez & Michel Gendreau & Ola Jabali & Walter Rei, 2019. "A local branching matheuristic for the multi-vehicle routing problem with stochastic demands," Journal of Heuristics, Springer, vol. 25(2), pages 215-245, April.
    4. Dimitrakos, T.D. & Kyriakidis, E.G., 2015. "A single vehicle routing problem with pickups and deliveries, continuous random demands and predefined customer order," European Journal of Operational Research, Elsevier, vol. 244(3), pages 990-993.
    5. Zhang, Junlong & Lam, William H.K. & Chen, Bi Yu, 2016. "On-time delivery probabilistic models for the vehicle routing problem with stochastic demands and time windows," European Journal of Operational Research, Elsevier, vol. 249(1), pages 144-154.
    6. Jorge E. Mendoza & Louis-Martin Rousseau & Juan G. Villegas, 2016. "A hybrid metaheuristic for the vehicle routing problem with stochastic demand and duration constraints," Journal of Heuristics, Springer, vol. 22(4), pages 539-566, August.
    7. Goodson, Justin C., 2015. "A priori policy evaluation and cyclic-order-based simulated annealing for the multi-compartment vehicle routing problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 241(2), pages 361-369.
    8. Briseida Sarasola & Karl F. Doerner & Verena Schmid & Enrique Alba, 2016. "Variable neighborhood search for the stochastic and dynamic vehicle routing problem," Annals of Operations Research, Springer, vol. 236(2), pages 425-461, January.
    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.
    10. Justin C. Goodson & Barrett W. Thomas & Jeffrey W. Ohlmann, 2016. "Restocking-Based Rollout Policies for the Vehicle Routing Problem with Stochastic Demand and Duration Limits," Transportation Science, INFORMS, vol. 50(2), pages 591-607, May.
    11. Kangzhou Wang & Shulin Lan & Yingxue Zhao, 2017. "A genetic-algorithm-based approach to the two-echelon capacitated vehicle routing problem with stochastic demands in logistics service," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(11), pages 1409-1421, November.
    12. 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.
    13. F. Hooshmand Khaligh & S.A. MirHassani, 2016. "A mathematical model for vehicle routing problem under endogenous uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 579-590, January.
    14. Benjamin Biesinger & Bin Hu & Günther R. Raidl, 2018. "A Genetic Algorithm in Combination with a Solution Archive for Solving the Generalized Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 52(3), pages 673-690, June.
    15. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2018. "The stochastic vehicle routing problem, a literature review, part I: models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 7(3), pages 193-221, September.
    16. Briseida Sarasola & Karl Doerner & Verena Schmid & Enrique Alba, 2016. "Variable neighborhood search for the stochastic and dynamic vehicle routing problem," Annals of Operations Research, Springer, vol. 236(2), pages 425-461, January.

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