A modified clustering search based genetic algorithm for the proactive electric vehicle routing problem

被引：0
|
作者：
El Hammouti, Issam [1 ]
Derqaoui, Khaoula [1 ]
El Merouani, Mohamed [1 ]
机构：
[1] Fac Sci Tetuan, Dept Math, Tetouan, Morocco
来源：
INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS | 2023年 / 14卷 / 04期
关键词：
Meta-heuristics; Mathematical modelling; Clustering; Genetic algorithm; Electric vehicle routing; Travel time uncertainty; TIME WINDOWS; HYBRID;
D O I：
10.5267/j.ijiec.2023.9.004
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this paper, an electric vehicle routing problem with time windows and under travel time uncertainty (U-EVRW) is addressed. The U-EVRW aims to find the optimal proactive routing plan of the electric vehicles under the travel time uncertainty during the route of the vehicles which is rarely studied in the literature. Furthermore, customer time windows, limited loading capacities and limited battery capacities constraints are also incorporated. A new mixed integer programming (MIP) model is formulated for the proposed U-EVRW. In addition to the commercial CPLEX Optimizer version 20.1.0, a modified Clustering Search based Genetic algorithm (MCSGA) is developed as a solution method. Numerical tests are conducted on the one hand to validate the effectiveness of the proposed MCSGA and on the other hand to analyze the impact of travel time uncertainty of the electric vehicle on the solutions quality.& COPY; 2023 by the authors; licensee Growing Science, Canada
引用
收藏
页码：609 / 622
页数：14
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