Logistical Planning for Electric Vehicles Under Time-Dependent Stochastic Traffic

For the benefit of global environmental preservation, electric vehicles (EVs) have been gradually accepted by people in the past few years. However, the technical problem of limited drivable range and long charging duration is still a major hurdle for the popularization of EVs, especially for commer...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2019-10, Vol.20 (10), p.3771-3781
Main Authors: Bi, Xiaowen, Tang, Wallace K. S.
Format: Article
Language:English
Subjects:
Algorithms
Analytical models
approximate dynamic programming
Batteries
Charging
Computer simulation
Electric vehicles
electric vehicles (EVs)
Heuristic algorithms
Logistical planning
Logistics
Planning
Route planning
Routing
Stochastic processes
Time dependence
time-dependent stochastic traffic
Traffic planning
Vehicle routing
Voltage control
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Summary:For the benefit of global environmental preservation, electric vehicles (EVs) have been gradually accepted by people in the past few years. However, the technical problem of limited drivable range and long charging duration is still a major hurdle for the popularization of EVs, especially for commercial usage. In this paper, a dynamic electric vehicle routing problem (D-EVRP) model is designed for planning the itinerary for goods delivery by the utilization of EVs in logistics industry. To reflect the real situation, the D-EVRP considers a time-dependent stochastic traffic condition and captures the discharging/charging pattern of an EV using an analytical battery model. Its aim is to minimize the overall service duration, subject to a variety of the state-of-art constraints common in EV routing problems. Furthermore, to address the D-EVRP, a hybrid rollout algorithm (HRA), which incorporates a dedicated pre-planning strategy and a rollout algorithm, is also proposed. The effectiveness of the HRA and benefits of incorporating the analytical battery model are justified by extensive simulations using the real-world D-EVRP instances.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2883791