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A simulation‐based optimization model for infrastructure planning for electric autonomous vehicle sharing
Author(s) -
Zhao Dongfang,
Li Xiaopeng,
Cui Jianxun
Publication year - 2021
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12506
Subject(s) - software deployment , genetic algorithm , electric vehicle , key (lock) , heuristic , computer science , simulation , exploit , operations research , engineering , computer security , quantum mechanics , machine learning , artificial intelligence , operating system , power (physics) , physics
New transportation technologies (e.g., electric autonomous vehicles [EAVs]) and operation paradigms (e.g., car sharing) are discussed, researched, and to a small degree also deployed in recent years in response to rising energy crises and aggravating traffic congestions. In this research, we present a station‐based car‐sharing service system that integrates both EAV technologies and car‐sharing operations. Based on the simulation model, a dynamic and time‐continuous optimization model seeking a near‐optimum design of charging station location and EAV deployment is developed. By discretizing the model, we proposed a Monte Carlo simulation model to evaluate the total system cost for a given location and vehicle deployment design. A heuristic approach based on the genetic algorithm is developed to solve the system design of station location and vehicle deployment. A numerical test in Yantai City, China, is conducted to illustrate the effectiveness of the proposed model and to draw managerial insights into how the key parameters affect the system design.