The vehicle routing problem with speed optimization for shared autonomous electric vehicles service. (November 2021)

• Record Type: Journal Article
• Authors: Ma, Bingshan; Hu, Dawei; Chen, Xiqiong; Wang, Yin; Wu, Xue
• Is Part Of: Computers & industrial engineering. Volume 161(2021)
• Publication Date: 2021-11
• Subjects: Shared vehicles -- Electric vehicles -- Autonomous vehicles -- Route optimization -- Speed optimization -- Adaptive large neighborhood search; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107614
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Route and speed are optimized for shared autonomous electric vehicles service. Realistic energy consumption model is considered in route optimization. Adaptive large neighborhood search algorithm is embedded with speed optimization. Speed optimization helps save time, energy, and even helps reduce travel distance. Abstract: The shared autonomous electric vehicles (SAEVs), the integration of car-sharing, autonomous technology, and electrification, are anticipated to be the popular choice for future urban travel, offering more flexible demand-responsive services than traditional shared mobility. This study develops a mixed-integer linear model to optimize the SAEVs service, where the electric vehicles (EVs) are powered by the battery swapping stations (BSSs). The innovation of the model is the speed optimization on the travel arc while optimizing the service routes of the SAEVs fleet. A minimized weighted objective containing the total travel distance, the total travel time and the total energy consumption is considered. An adaptive large neighborhood search (ALNS) algorithm embedded in the speed optimization subroutine is designed to find the optimal solution. The good computational performance of ALNS is demonstrated through numerical comparisons to the solutions found by CPLEX and the best-known solutions of the similar dial-a-ride problem (DARP). The adapted set of instances was employed in the comparative experiment, from which the benefits of speed optimization for saving the objectives are obtained. Sensitivity analysis was performed on the minimum battery level, battery capacity, and the weight of energy consumption in the objective. The results provide SAEVs operators with alternative cost-saving solutions. Larger capacity batteries and lower minimum battery levels help reduce the travel distance, while smaller capacity batteries and feasible lower speeds help to save energy consumption.