Determining the Optimal Route of Electric Vehicle Using a Hybrid Algorithm Based on Fuzzy Dynamic Programming

Velimirović, Lazar; Janjić, Aleksandar; Vranic, Petar; Velimirović, Jelena D.; Petkovski, Ivana

doi:10.1109/tfuzz.2022.3205045

Cited by 7 publications

(2 citation statements)

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“…The complex ELRP can be seen as an orienteering problem, which is a combination optimization problem combining node selection, neighbor searching, the shortest Hamiltonian path, minimum cost, and maximizing the total score of the objective function [16,31,32]. Time windows and time-dependent scores are important methods for the ELRP, where a set of charging paths can be built to maximize the collected scores [4,11,17].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The ability or behavior rules of each individual in a heuristic algorithm are very simple, so the realization of heuristic intelligence is relatively convenient and has the characteristics of simplicity. Examples include the genetic algorithm (GA) [5,36], artificial fish swarm algorithm (AFSA) [8], fuzzy logic (FL) [9,32], simulated annealing (SA) [12], particle swarm optimization (PSO) [18,37], deep reinforcement learning (DRL) [19,20,38], ant colony optimization (ACO) [21,39], machine learning (ML) and artificial neural networks (ANNs) [22,38,40], artificial bee colony (ABC) [23,41], grey wolf optimization (GWO) [24], artificial plant community (APC) [42], whale optimization algorithm(WOA) [43], and artificial slime mold (ASM) [13,44,45]. The heuristic algorithms can help us obtain a satisfactory feasible solution in a short time, but the deviation degree between the feasible solution and the optimal solution cannot be predicted.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Artificial Physarum polycephalum Colony for the Electric Location-Routing Problem

Cai,

Wang,

et al. 2023

Sustainability

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Electric vehicles invented for environmental sustainability are prone to adverse impacts on environmental sustainability due to the location and construction of their charging facilities. In this article, an artificial Physarum polycephalum colony is proposed to solve the novel challenging problem. First, the electric location-routing problem is established as a multi-objective network panning model with electric constraints to provide the optimal charging infrastructure layout, electric vehicle maintenance costs, and traffic conditions. The electric facility location problem and vehicle routing problem are integrated by integer programming, which considers the total distance, total time, total cost, total number of electric vehicles, and order fill rate. Second, an artificial Physarum polycephalum colony is introduced to solve the complex electric location-routing problem and includes the two basic operations of expansion and contraction. In the expansion operation, the optimal parent individuals will generate more offspring individuals, so as to expand the population size. In the contraction operation, only individuals with high fitness will be selected to survive through a merge sorting algorithm, resulting in a decrease in population size to the initial value. Through the iterative computing of the two main operations, the proposed artificial Physarum polycephalum colony can finally find the optimal solution to the objective function. Third, a benchmark test is designed for the electric location-routing problem by extracting the real road network from Tokyo, and the experimental results prove the effectiveness and applicability of this work.

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