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Constructive Heuristics for the Multicompartment Vehicle Routing Problem with Stochastic Demands

Author

Listed:
  • Jorge E. Mendoza

    (Équipe Optimisation des Systèmes de Production et Logistiques, LISA (EA CNRS 4094), Université Catholique de l'Ouest, 49008 Angers, France)

  • Bruno Castanier

    (Équipe Systèmes Logistiques et de Production, IRCCyN (UMR CNRS 6597), École des Mines de Nantes, 44307 Nantes Cedex 3, France)

  • Christelle Guéret

    (Équipe Systèmes Logistiques et de Production, IRCCyN (UMR CNRS 6597), École des Mines de Nantes, 44307 Nantes Cedex 3, France)

  • Andrés L. Medaglia

    (Centro para la Optimización y Probabilidad Aplicada (COPA), Departamento de Ingeniería Industrial, Universidad de los Andes, AA 4976 Bogotá, Colombia)

  • Nubia Velasco

    (Centro para la Optimización y Probabilidad Aplicada (COPA), Departamento de Ingeniería Industrial, Universidad de los Andes, AA 4976 Bogotá, Colombia)

Abstract

The vehicle routing problem with stochastic demands (VRPSD) consists of designing transportation routes of minimal expected cost to satisfy a set of customers with random demands of known probability distribution. This paper tackles a generalization of the VRPSD known as the multicompartment VRPSD (MC-VRPSD), a problem in which each customer demands several products that, because of incompatibility constraints, must be loaded in independent vehicle compartments. To solve the problem, we propose three simple and effective constructive heuristics based on a stochastic programming with recourse formulation. One of the heuristics is an extension to the multicompartment scenario of a savings-based algorithm for the VRPSD; the other two are different versions of a novel look-ahead heuristic that follows a route-first, cluster-second approach. In addition, to enhance the performance of the heuristics these are coupled with a post-optimization procedure based on the classical 2-Opt heuristic. The three algorithms were tested on instances of up to 200 customers from the MC-VRPSD and VRPSD literature. The proposed heuristics unveiled 26 and 12 new best known solutions for a set of 180 MC-VRPSD problems and a 40-instance testbed for the VRPSD, respectively.

Suggested Citation

  • Jorge E. Mendoza & Bruno Castanier & Christelle Guéret & Andrés L. Medaglia & Nubia Velasco, 2011. "Constructive Heuristics for the Multicompartment Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 45(3), pages 346-363, August.
    • Handle: RePEc:inm:ortrsc:v:45:y:2011:i:3:p:346-363
    DOI: 10.1287/trsc.1100.0353
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    References listed on IDEAS

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    1. Henke, Tino & Speranza, M. Grazia & Wäscher, Gerhard, 2015. "The multi-compartment vehicle routing problem with flexible compartment sizes," European Journal of Operational Research, Elsevier, vol. 246(3), pages 730-743.
    2. Tino Henke & M. Grazia Speranza & Gerhard Wäscher, 2019. "A branch-and-cut algorithm for the multi-compartment vehicle routing problem with flexible compartment sizes," Annals of Operations Research, Springer, vol. 275(2), pages 321-338, April.
    3. Alexander Hübner & Manuel Ostermeier, 2019. "A Multi-Compartment Vehicle Routing Problem with Loading and Unloading Costs," Service Science, INFORMS, vol. 53(1), pages 282-300, February.
    4. Heßler, Katrin, 2021. "Exact algorithms for the multi-compartment vehicle routing problem with flexible compartment sizes," European Journal of Operational Research, Elsevier, vol. 294(1), pages 188-205.
    5. Toni Greif & Nikolai Stein & Christoph M. Flath, 2023. "Information Value Analysis for Real-Time Silo Fill-Level Monitoring," Interfaces, INFORMS, vol. 53(4), pages 283-294, July.
    6. Tino Henke & M. Grazia Speranza & Gerhard Wäscher, 2014. "The Multi-Compartment Vehicle Routing Problem with Flexible Compartment Sizes," FEMM Working Papers 140006, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    7. 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.
    8. 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.
    9. Nasrin Asgari & Mohsen Rajabi & Masoumeh Jamshidi & Maryam Khatami & Reza Zanjirani Farahani, 2017. "A memetic algorithm for a multi-objective obnoxious waste location-routing problem: a case study," Annals of Operations Research, Springer, vol. 250(2), pages 279-308, March.
    10. Pillac, Victor & Gendreau, Michel & Guéret, Christelle & Medaglia, Andrés L., 2013. "A review of dynamic vehicle routing problems," European Journal of Operational Research, Elsevier, vol. 225(1), pages 1-11.
    11. Katrin Heßler, 2020. "Exact Algorithms for the Multi-Compartment Vehicle Routing Problem with Flexible Compartment Sizes," Working Papers 2007, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    12. Alinaghian, Mahdi & Shokouhi, Nadia, 2018. "Multi-depot multi-compartment vehicle routing problem, solved by a hybrid adaptive large neighborhood search," Omega, Elsevier, vol. 76(C), pages 85-99.
    13. 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.
    14. Ostermeier, Manuel & Henke, Tino & Hübner, Alexander & Wäscher, Gerhard, 2021. "Multi-compartment vehicle routing problems: State-of-the-art, modeling framework and future directions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 799-817.
    15. SteadieSeifi, M. & Dellaert, N.P. & Nuijten, W. & Van Woensel, T., 2017. "A metaheuristic for the multimodal network flow problem with product quality preservation and empty repositioning," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 321-344.
    16. 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.
    17. Henriette Koch & Tino Henke & Gerhard Wäscher, 2016. "A Genetic Algorithm for the Multi-Compartment Vehicle Routing Problem with Flexible Compartment Sizes," FEMM Working Papers 160004, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    18. Markov, Iliya & Bierlaire, Michel & Cordeau, Jean-François & Maknoon, Yousef & Varone, Sacha, 2018. "A unified framework for rich routing problems with stochastic demands," Transportation Research Part B: Methodological, Elsevier, vol. 114(C), pages 213-240.
    19. Ostermeier, Manuel & Hübner, Alexander, 2018. "Vehicle selection for a multi-compartment vehicle routing problem," European Journal of Operational Research, Elsevier, vol. 269(2), pages 682-694.
    20. Tino Henke & Grazia Speranza & Gerhard Wäscher, 2017. "A Branch-and-Cut Algorithm for the Multi Compartment vehicle Routing Problem with Flexbile Compartment Sizes," FEMM Working Papers 170004, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.

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