Multiple Types of Plug-In Charging Facilities’ Location-Routing Problem with Time Windows for Mobile Charging Vehicles

Shaohua Cui, Hui Zhao and Cuiping Zhang
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Shaohua Cui: MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Hui Zhao: MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Cuiping Zhang: Computing Center, Beijing Information Science & Technology University, Beijing 100192, China

Sustainability, 2018, vol. 10, issue 8, 1-26

Abstract: Increasing attention is being paid to the technology of battery electric vehicles (BEVs) because of their environmental friendliness. However, their short range, extended recharging times, and insufficient charging facilities hinder the improvement in the market share of BEVs. As a remedy, this paper presents a novel approach to providing a service for the battery charge replenishment of BEVs. Instead of using traditional alternative methods by only providing a charging service in a fixed location, such as battery-swapping and charging lanes, the novel charge replenishment is provided by mobile charging vehicles (MCVs), which could offer a charging service at any time and at location requested. To consider the limited running range and the opportunity to recharge from MCVs, as well as to determine the location strategy of multiple types of plug-in charging facility locations and the routing plan of the MCVs simultaneously, the location routing problem (LRP) that can integrate two decision levels, with a strategic level (location) and tactical level (routing), is applied. Then, we present the multiple types of plug-in charging facilities’ location-routing problem with time windows for mobile charging vehicles (MTPCF-LRPwTW-MCVs), and formulate the MTPCF-LRPwTW-MCVs as a mixed integer linear program for the convenience of solving. To demonstrate the model, test instances are designed and computational results are presented. Furthermore, sensitivity analyses on battery capacity, recharging rate, and so on, are also examined. The results show that with the increase of the battery capacity or the improvement of the charging rate of the charging facilities, the service efficiency of the MCVs can reasonably be improved. Therefore, the proposed method could be used in real world problems.

Keywords: mobile charging services; battery electric vehicles; location-routing problem; time windows (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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