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Solving the Vehicle Routing Problem with Stochastic Demands using the Cross-Entropy Method

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  • Krishna Chepuri
  • Tito Homem-de-Mello

Abstract

An alternate formulation of the classical vehicle routing problem with stochastic demands ( VRPSD) is considered. We propose a new heuristic method to solve the problem, based on the Cross-Entropy method. In order to better estimate the objective function at each point in the domain, we incorporate Monte Carlo sampling. This creates many practical issues, especially the decision as to when to draw new samples and how many samples to use. We also develop a framework for obtaining exact solutions and tight lower bounds for the problem under various conditions, which include specific families of demand distributions. This is used to assess the performance of the algorithm. Finally, numerical results are presented for various problem instances to illustrate the ideas. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Krishna Chepuri & Tito Homem-de-Mello, 2005. "Solving the Vehicle Routing Problem with Stochastic Demands using the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 153-181, February.
    • Handle: RePEc:spr:annopr:v:134:y:2005:i:1:p:153-181:10.1007/s10479-005-5729-7
    DOI: 10.1007/s10479-005-5729-7
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    5. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
    6. 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.
    7. Emelogu, Adindu & Chowdhury, Sudipta & Marufuzzaman, Mohammad & Bian, Linkan & Eksioglu, Burak, 2016. "An enhanced sample average approximation method for stochastic optimization," International Journal of Production Economics, Elsevier, vol. 182(C), pages 230-252.
    8. Walter Rei & Michel Gendreau & Patrick Soriano, 2010. "A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 44(1), pages 136-146, February.
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    11. Prasanna Balaprakash & Mauro Birattari & Thomas Stützle & Marco Dorigo, 2015. "Estimation-based metaheuristics for the single vehicle routing problem with stochastic demands and customers," Computational Optimization and Applications, Springer, vol. 61(2), pages 463-487, June.
    12. 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.
    13. Florio, Alexandre M. & Hartl, Richard F. & Minner, Stefan, 2020. "Optimal a priori tour and restocking policy for the single-vehicle routing problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 285(1), pages 172-182.
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    16. Novoa, Clara & Storer, Robert, 2009. "An approximate dynamic programming approach for the vehicle routing problem with stochastic demands," European Journal of Operational Research, Elsevier, vol. 196(2), pages 509-515, July.
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    19. Florio, Alexandre M. & Gendreau, Michel & Hartl, Richard F. & Minner, Stefan & Vidal, Thibaut, 2023. "Recent advances in vehicle routing with stochastic demands: Bayesian learning for correlated demands and elementary branch-price-and-cut," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1081-1093.
    20. Rafael Dawid & David McMillan & Matthew Revie, 2018. "Decision Support Tool for Offshore Wind Farm Vessel Routing under Uncertainty," Energies, MDPI, vol. 11(9), pages 1-17, August.
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    23. Zamar, David S. & Gopaluni, Bhushan & Sokhansanj, Shahab, 2017. "Optimization of sawmill residues collection for bioenergy production," Applied Energy, Elsevier, vol. 202(C), pages 487-495.

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