A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times
Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/94086 |
| Acceso en línea: | http://hdl.handle.net/10609/94086 |
| Access Level: | acceso abierto |
| Palabra clave: | vehicle routing problem electric vehicles green transport and logistics smart cities simheuristics biased-randomized heuristics heurística aleatòria problema de rutes de vehicles vehicles elèctrics logística i transport verd ciutats intel·ligents simheurística problema de rutas de vehículos vehículos eléctricos logística y transporte verde ciudades inteligentes heurística aleatoria Electric vehicles Vehicles elèctrics Vehículos eléctricos |
| Sumario: | Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected timebased cost required to complete the freight distribution plan. In order to design reliable routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans. |
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