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A biased-randomized variable neighborhood search for sustainable multi-depot vehicle routing problems

Author

Listed:
  • Lorena Reyes-Rubiano

    (Public University of Navarra)

  • Laura Calvet

    (Open University of Catalonia
    Euncet Business School)

  • Angel A. Juan

    (Open University of Catalonia
    Euncet Business School)

  • Javier Faulin

    (Public University of Navarra)

  • Lluc Bové

    (Open University of Catalonia
    Euncet Business School)

Abstract

Urban freight transport is becoming increasingly complex due to a boost in the volume of products distributed and the associated number of delivery services. In addition, stakeholders’ preferences and city logistics dynamics affect the freight flow and the efficiency of the delivery process in downtown areas. In general, transport activities have a significant and negative impact on the environment and citizens’ welfare, which motivates the need for sustainable transport planning. This work proposes a metaheuristic-based approach for tackling an enriched multi-depot vehicle routing problem in which economic, environmental, and social dimensions are considered. Our approach integrates biased-randomization strategies within a variable neighborhood search framework in order to better guide the searching process. A series of computational experiments illustrates how the aforementioned dimensions can be integrated in realistic transport operations. Also, the paper discusses how the cost values change as different dimensions are prioritized.

Suggested Citation

  • Lorena Reyes-Rubiano & Laura Calvet & Angel A. Juan & Javier Faulin & Lluc Bové, 2020. "A biased-randomized variable neighborhood search for sustainable multi-depot vehicle routing problems," Journal of Heuristics, Springer, vol. 26(3), pages 401-422, June.
    • Handle: RePEc:spr:joheur:v:26:y:2020:i:3:d:10.1007_s10732-018-9366-0
    DOI: 10.1007/s10732-018-9366-0
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    References listed on IDEAS

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    Cited by:

    1. Elnaz Ghorbani & Tristan Fluechter & Laura Calvet & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2023. "Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy," Energies, MDPI, vol. 16(3), pages 1-19, January.
    2. Alice Vasconcelos Nobre & Caio Cézar Rodrigues Oliveira & Denilson Ricardo de Lucena Nunes & André Cristiano Silva Melo & Gil Eduardo Guimarães & Rosley Anholon & Vitor William Batista Martins, 2022. "Analysis of Decision Parameters for Route Plans and Their Importance for Sustainability: An Exploratory Study Using the TOPSIS Technique," Logistics, MDPI, vol. 6(2), pages 1-12, May.

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