Open AccessThe Mathematical Model and an Genetic Algorithm for the Two-Echelon Electric Vehicle Routing Problem
Author(s) -
Yue Zhang,
Shenghan Zhou,
Xinpeng Ji,
Bang Chen,
Houxiang Liu,
Yiyong Xiao,
Wenbing Chang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1813/1/012006
Subject(s) - vehicle routing problem , simulated annealing , genetic algorithm , computer science , electric vehicle , mathematical optimization , routing (electronic design automation) , heuristic , path (computing) , shortest path problem , operations research , algorithm , engineering , mathematics , artificial intelligence , computer network , graph , power (physics) , physics , quantum mechanics , theoretical computer science , machine learning
In order to cope with the challenges of high cargo load and high timeliness distribution in logistics industry, as well as to alleviate the current situation of oil resource depletion and air pollution, this study established a mathematical model of two-echelon electric vehicle routing problem (2E-EVRP) and design a heuristic algorithm. The 2E-EVRP can be divided into the multiple depot vehicle routing problem (MDEVRP) and the split delivery vehicle routing problem (SDVRP). The proposed genetic algorithm is used to solve the MDEVRP, and the actual case of a logistics company in Beijing is taken as the calculation experiment, so as to verify the feasibility of the proposed algorithm and provide decision-making reference for the development of logistics enterprises. The results show that the total path length obtained by the proposed algorithm is optimized by 20.82 kilometers compared with the traditional simulated annealing algorithm.