Tabu Search with Multiple Decision Levels for Solving Heterogeneous Fleet Pollution Routing Problem

Bryan F. Salcedo-Moncada, Daniel Morillo-Torres, Gustavo Gatica

    Producción científica: Capítulo del libro/informe/acta de congresoPonencia publicada en las memorias del evento con ISBNrevisión exhaustiva


    Organizations, in order to gain a competitive advantage, must improve their logistics performance along with the planning and distribution of their goods. Thus, they face significant challenges in managing their orders to be delivered on time. However, transportation is responsible for of the emissions of the total polluting gases in the atmosphere. Therefore, there is a growing interest to investigate methods to optimize logistics and to consider environmental aspects. However, the literature only considers realistic system characteristics such as: different vehicles and speeds, time windows and route inclination. For this reason, the focus is on the solution of an extension with a heterogeneous fleet and discrete speeds of the Vehicle Routing Pollution Problem (PRP), whose objective is the reduction of greenhouse gases (GHG). Based on the MEET model, the main polluting gases with the greatest impact on health are measured: carbon dioxide, nitrogen dioxide and carbon monoxide (CO). For its solution, a Tabu Search metaheuristic is proposed with different decision levels: node sequence, assigned speeds and vehicles used, from different neighborhood structures. Finally, the balance between exploration and exploitation is achieved by incorporating favorable attributes to the created solutions. The proposed metaheuristic achieves efficient results both in total logistic cost and in emissions released to the environment.

    Idioma originalInglés
    Título de la publicación alojadaMetaheuristics - 14th International Conference, MIC 2022, Proceedings
    EditoresLuca Di Gaspero, Paola Festa, Amir Nakib, Mario Pavone
    EditorialSpringer Science and Business Media Deutschland GmbH
    Número de páginas15
    ISBN (versión impresa)9783031265037
    EstadoPublicada - 2023
    Evento14th Metaheuristics International Conference, MIC 2022 - Ortigia-Syracuse, Italia
    Duración: 11 jul. 202214 jul. 2022

    Serie de la publicación

    NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volumen13838 LNCS
    ISSN (versión impresa)0302-9743
    ISSN (versión digital)1611-3349


    Conferencia14th Metaheuristics International Conference, MIC 2022

    Nota bibliográfica

    Publisher Copyright:
    © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


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