Logistical Planning for Electric Vehicles Under Time-Dependent Stochastic Traffic

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

35 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)3771-3781
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume20
Issue number10
Online published12 Dec 2018
Publication statusPublished - Oct 2019

Abstract

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.

Research Area(s)

  • approximate dynamic programming., electric vehicles (EVs), Logistical planning, time-dependent stochastic traffic