Erdem, MehmetKoc, CagriIndustrial Engineering2024-07-052024-07-052019460959-65261879-178610.1016/j.jclepro.2019.06.2362-s2.0-85068449986https://doi.org/10.1016/j.jclepro.2019.06.236https://hdl.handle.net/20.500.14411/3480Koç, Çağrı/0000-0002-7377-204X; Erdem, Mehmet/0000-0003-4396-2149The road transportation is one of the largest contributors to greenhouse gas emissions globally, and rapid urbanisation increases the environmental and economic challenges. Electric vehicles support green supply chain and clean routing operations when compared with the traditional fossil fuel-powered vehicles. This paper analyses a variant of the home health care routing problem in which a group of health care workers performs a requested number of jobs by using electric vehicles. The problem considers multi-depot, heterogeneous fleet, time windows, preferences, competencies, connected activities, the range of electric vehicles, charging status, and charge strategies. We develop a hybrid metaheuristic which successfully combines genetic algorithm and a variable neighbourhood descent, and offer several algorithmic procedures tailored to handle the rich constraints of the problem. Extensive computational experiments on small, medium and large-scale instances have shown that the hybrid metaheuristic is effective on the problem. (C) 2019 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessGreen transportationHome health careVehicle routingElectric vehiclesArticleQ123414711483WOS:000483406000120