The automotive industry is witnessing a surge in the production of electric vehicles (EVs) driven by stringent emission regulations. Despite this growth, heavy-duty truck fleets, particularly in waste collection, remain predominantly combustion-based ones. Waste collection is critical in urban environments, presenting unique challenges due to confined operational regions. One alternative to increase EVs in waste collection is to substitute the smaller truck fleets used for waste collection in constrained environments, such as narrow streets, by EVs. In this paper, we present a new formulation for the waste collection problem that considers a truck fleet comprised of smaller EVs and regular combustion trucks. The smaller trucks are proposed for the waste collection of specific sites (i.e. dumpsters in narrow streets). Our formulation considers battery limitations of electric trucks and flexible time windows for the waste collection task. The solution was validated by comparing the emission of CO2 and collection costs of a fleet comprised solely of combustion trucks and the hybrid fleet proposed here. The results showed that using a hybrid fleet significantly reduced waste collection costs and environmental impacts.


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    Titel :

    Optimizing Waste Collection in Constrained Urban Spaces: A Hybrid Fleet Approach


    Weitere Titelangaben:

    Communic.Comp.Inf.Science


    Beteiligte:
    Pereira, Ana I. (Herausgeber:in) / Fernandes, Florbela P. (Herausgeber:in) / Coelho, João P. (Herausgeber:in) / Teixeira, João P. (Herausgeber:in) / Lima, José (Herausgeber:in) / Pacheco, Maria F. (Herausgeber:in) / Lopes, Rui P. (Herausgeber:in) / Álvarez, Santiago T. (Herausgeber:in) / Silva, A. S. (Autor:in) / Lima, José (Autor:in)

    Kongress:

    International Conference on Optimization, Learning Algorithms and Applications ; 2024 ; Tenerife, Spain July 24, 2024 - July 26, 2024



    Erscheinungsdatum :

    26.12.2024


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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