Short- and mid-term evaluation of the use of electric vehicles in urban freight transport collaborative networks: a case study

Despite its negative impacts, freight transportation is a primary component of all supply chains. Decision makers have considered diverse strategies, such as Horizontal Collaboration (HC) and the usage of alternative types of vehicles, to reduce overall cost and the related environmental and social...

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Detalles Bibliográficos
Autores: Muñoz Villamizar, Andrés, Quintero Araújo, Carlos L., Montoya Torres, Jairo R., Faulín Fajardo, Javier
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Institución:Universidad Pública de Navarra
Repositorio:Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
OAI Identifier:oai:academica-e.unavarra.es:2454/36070
Acceso en línea:https://hdl.handle.net/2454/36070
Access Level:acceso abierto
Palabra clave:Urban freight transport
Horizontal collaboration
Sustainability
Electric vehicles
Multi-objective optimisation
Case study
Descripción
Sumario:Despite its negative impacts, freight transportation is a primary component of all supply chains. Decision makers have considered diverse strategies, such as Horizontal Collaboration (HC) and the usage of alternative types of vehicles, to reduce overall cost and the related environmental and social impacts. This paper assesses the implementation of an electric fleet of vehicles in urban goods distribution under HC strategy between carriers. A biased randomisation based algorithm is used to solve the problem with a multi-objective function to explore the relationships between both delivery and environmental costs. Real data from the city of Bogota, Colombia are used to validate this approach. Experiments with different costs and demands projections are performed to analyse short- and medium-term impacts related to the usage of electric vehicles in collaborative networks. Results show that the optimal selection of vehicle types depends considerably on the time horizon evaluation and demand variation.