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The Stochastic and Dynamic Traveling Purchaser Problem

Author

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  • E. Angelelli

    (Department of Economics and Management, University of Brescia, 25122 Brescia, Italy)

  • R. Mansini

    (Department of Information Engineering, University of Brescia, 25123 Brescia, Italy)

  • M. Vindigni

    (Department of Economics and Management, University of Brescia, 25122 Brescia, Italy)

Abstract

In this paper, we analyze a dynamic and stochastic variant of the traveling purchaser problem where quantity available for each product in each market decreases over time according to a stochastic process. The multiobjective nature of the problem is faced through a hierarchical evaluation of the different objectives. We introduce three variants of a heuristic approach using reoptimization to exploit new information as it becomes available. The proposed approaches are studied under different operating scenarios characterized by the communication technologies at hand and by the level of information available on the state of the world. Extensive computational results show how communication and a good level of information represent valuable tools for a decision maker, and provide interesting guidelines for decision makers involved with similar stochastic and dynamic problems.

Suggested Citation

  • E. Angelelli & R. Mansini & M. Vindigni, 2016. "The Stochastic and Dynamic Traveling Purchaser Problem," Transportation Science, INFORMS, vol. 50(2), pages 642-658, May.
  • Handle: RePEc:inm:ortrsc:v:50:y:2016:i:2:p:642-658
    DOI: 10.1287/trsc.2015.0627
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    Cited by:

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    2. Archetti, Claudia & Bertazzi, Luca & Laganà, Demetrio & Vocaturo, Francesca, 2017. "The Undirected Capacitated General Routing Problem with Profits," European Journal of Operational Research, Elsevier, vol. 257(3), pages 822-833.
    3. Wang, Jianxin & Lim, Ming K. & Liu, Weihua, 2024. "Promoting intelligent IoT-driven logistics through integrating dynamic demand and sustainable logistics operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
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    6. Caballero, William N. & Lunday, Brian J. & Meissner, Finn, 2024. "Regulating the rebound effect in the traveling purchaser problem," European Journal of Operational Research, Elsevier, vol. 317(3), pages 660-677.
    7. Manerba, Daniele & Mansini, Renata & Riera-Ledesma, Jorge, 2017. "The Traveling Purchaser Problem and its variants," European Journal of Operational Research, Elsevier, vol. 259(1), pages 1-18.
    8. Mingyu Xiao & Jianan Zhang & Weibo Lin, 0. "Parameterized algorithms and complexity for the traveling purchaser problem and its variants," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-17.
    9. Mingyu Xiao & Jianan Zhang & Weibo Lin, 2022. "Parameterized algorithms and complexity for the traveling purchaser problem and its variants," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2269-2285, November.
    10. Roohnavazfar, Mina & Manerba, Daniele & De Martin, Juan Carlos & Tadei, Roberto, 2019. "Optimal paths in multi-stage stochastic decision networks," Operations Research Perspectives, Elsevier, vol. 6(C).

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