Using Evolutionary Routing Optimisation to Transition to Electric Vehicle Fleets

被引:0
|
作者
Chitty, Darren M. [1 ]
Yates, William B. [1 ]
Keedwell, Ed [1 ]
机构
[1] Univ Exeter, Coll Engn Math & Phys Sci, Exeter EX4 4QF, Devon, England
关键词
Genetic Algorithm; Electric Vehicles; CVRP; EVRP; DEPOT; NUMBER;
D O I
10.1007/978-3-031-55568-8_41
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Global warming caused by high carbon dioxide emissions is a critical issue. Transportation systems are a major contributor especially commercial vehicle fleets which typically use highly polluting diesel. Internal combustion engine vehicles (ICEVs) also emit particulates which are hazardous to health. Consequently, there is a drive to adopt electric vehicles (EVs) as greener and cheaper transport. For commercial vehicle fleets transitioning to EVs is problematic due to limited range and capacity factors. However, optimisation of EV routes can mitigate these issues. This paper explores using evolutionary optimisation to route EVs for a real-world delivery company. Moreover, the method is used to explore the types and numbers of EVs that provide best performance when minimising distance, costs and emissions. Experiments demonstrate using EVs to minimise cost and emissions is not straightforward. In fact, a mix of EVs and ICEVs provides best cost savings with reduced emissions.
引用
收藏
页码:489 / 501
页数:13
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