Collaborative multidepot electric vehicle routing problem with time windows and shared charging stations

Logistics distribution using electric vehicles (EVs) is gradually becoming popular in urban logistics networks with the promise of carbon peak and carbon neutrality. A collaborative mechanism among multiple depots can effectively increase the efficiency and sustainability of a logistics network. Sha...

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Veröffentlicht in:Expert systems with applications Jg. 219; S. 119654
Hauptverfasser: Wang, Yong, Zhou, Jingxin, Sun, Yaoyao, Fan, Jianxin, Wang, Zheng, Wang, Haizhong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.06.2023
Schlagworte:
Bi-objective nonlinear programming model
Collaborative mechanism
Improved multi-objective genetic algorithm with tabu search
Multidepot electric vehicle routing problem with time windows
Shared charging stations
Multidepot electric vehicle routing problem with time windows
Improved multi-objective genetic algorithm with tabu search
Shared charging stations
Bi-objective nonlinear programming model
Collaborative mechanism
ISSN:0957-4174, 1873-6793
Online-Zugang:Volltext
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Abstract Logistics distribution using electric vehicles (EVs) is gradually becoming popular in urban logistics networks with the promise of carbon peak and carbon neutrality. A collaborative mechanism among multiple depots can effectively increase the efficiency and sustainability of a logistics network. Sharing charging stations (CSs) is presented as a critical strategy to improve the efficiency of logistics networks and facilitate collaboration further. Solving a collaborative multidepot EV routing problem with time windows and shared charging stations (CMEVRPTW-SCS) involves centralized transportation services among multiple depots, coordinated delivery services among multiple depots and the charging needs of EVs. A bi-objective nonlinear programming model for CMEVRPTW-SCS is formulated to minimize the total operating cost and number of EVs, wherein the operating cost contains transportation costs among depots and vehicle routing costs in each depot. Gaussian mixture clustering algorithm is developed to cluster customers and assign them to depots probabilistically to reduce the computational complexity, and an improved multi-objective genetic algorithm with tabu search (IMOGA-TS) is designed to optimize the EV routes and charging locations in CMEVRPTW-SCS. With the combination of local and global searches and the improvement of solutions at each iteration by TS, the proposed hybrid algorithm can significantly accelerate the efficient exploration of optimal solutions. The comparative results of the IMOGA-TS with multi-objective genetic algorithm, non-dominated sorting genetic algorithm-II and multi-objective harmony search algorithm show the superiority of the proposed algorithm in solving CMEVRPTW-SCS. Then, a Shapley value model based on game theoretical methods is proposed to find the best profit allocation scheme. A real-world case study of CMEVRPTW-SCS in Chongqing City, China is conducted, and three scenarios of various collaborative alliances and 11 schemes with and without shared CSs are further discussed. The experimental results show that the proposed methods are of practical significance in reducing operating costs, improving transportation efficiency, and achieving sustainable operation in EV-based logistics distribution networks.
AbstractList Logistics distribution using electric vehicles (EVs) is gradually becoming popular in urban logistics networks with the promise of carbon peak and carbon neutrality. A collaborative mechanism among multiple depots can effectively increase the efficiency and sustainability of a logistics network. Sharing charging stations (CSs) is presented as a critical strategy to improve the efficiency of logistics networks and facilitate collaboration further. Solving a collaborative multidepot EV routing problem with time windows and shared charging stations (CMEVRPTW-SCS) involves centralized transportation services among multiple depots, coordinated delivery services among multiple depots and the charging needs of EVs. A bi-objective nonlinear programming model for CMEVRPTW-SCS is formulated to minimize the total operating cost and number of EVs, wherein the operating cost contains transportation costs among depots and vehicle routing costs in each depot. Gaussian mixture clustering algorithm is developed to cluster customers and assign them to depots probabilistically to reduce the computational complexity, and an improved multi-objective genetic algorithm with tabu search (IMOGA-TS) is designed to optimize the EV routes and charging locations in CMEVRPTW-SCS. With the combination of local and global searches and the improvement of solutions at each iteration by TS, the proposed hybrid algorithm can significantly accelerate the efficient exploration of optimal solutions. The comparative results of the IMOGA-TS with multi-objective genetic algorithm, non-dominated sorting genetic algorithm-II and multi-objective harmony search algorithm show the superiority of the proposed algorithm in solving CMEVRPTW-SCS. Then, a Shapley value model based on game theoretical methods is proposed to find the best profit allocation scheme. A real-world case study of CMEVRPTW-SCS in Chongqing City, China is conducted, and three scenarios of various collaborative alliances and 11 schemes with and without shared CSs are further discussed. The experimental results show that the proposed methods are of practical significance in reducing operating costs, improving transportation efficiency, and achieving sustainable operation in EV-based logistics distribution networks.
ArticleNumber 119654
Author Wang, Haizhong
Zhou, Jingxin
Sun, Yaoyao
Fan, Jianxin
Wang, Zheng
Wang, Yong
Author_xml – sequence: 1
  givenname: Yong
  orcidid: 0000-0001-7511-8888
  surname: Wang
  fullname: Wang, Yong
  email: yongwx@cqjtu.edu.cn
  organization: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
– sequence: 2
  givenname: Jingxin
  orcidid: 0000-0002-3969-3057
  surname: Zhou
  fullname: Zhou, Jingxin
  organization: School of Economics and Management, Chongqing Jiaotong University, Chongqing 400074, China
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  givenname: Yaoyao
  surname: Sun
  fullname: Sun, Yaoyao
  organization: School of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
– sequence: 4
  givenname: Jianxin
  surname: Fan
  fullname: Fan, Jianxin
  email: jxfanw@cqjtu.edu.cn
  organization: School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing 400074, China
– sequence: 5
  givenname: Zheng
  surname: Wang
  fullname: Wang, Zheng
  email: drwz@dlut.edu.cn
  organization: School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
– sequence: 6
  givenname: Haizhong
  surname: Wang
  fullname: Wang, Haizhong
  email: Haizhong.Wang@oregonstate.edu
  organization: School of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97330, USA
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Keywords Multidepot electric vehicle routing problem with time windows
Improved multi-objective genetic algorithm with tabu search
Shared charging stations
Bi-objective nonlinear programming model
Collaborative mechanism
Language English
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Snippet Logistics distribution using electric vehicles (EVs) is gradually becoming popular in urban logistics networks with the promise of carbon peak and carbon...
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StartPage 119654
SubjectTerms Bi-objective nonlinear programming model
Collaborative mechanism
Improved multi-objective genetic algorithm with tabu search
Multidepot electric vehicle routing problem with time windows
Shared charging stations
Title Collaborative multidepot electric vehicle routing problem with time windows and shared charging stations
URI https://dx.doi.org/10.1016/j.eswa.2023.119654
Volume 219
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