Integrating NSGA-II and Q-learning for Solving the Multi-objective Electric Vehicle Routing Problem with Battery Swapping Stations

Navigating the challenges of the Electric Vehicle Routing Problem with Battery Swapping Stations (EVRP-BSS), this work is centered on a multi-objective optimization task, simultaneously minimizing battery swap costs and energy consumption costs. Given the intricate nature of this problem and its rea...

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Vydáno v:International journal of ITS research Ročník 23; číslo 2; s. 840 - 856
Hlavní autoři: Haddad, Anouar, Tlili, Takwa, Dahmani, Nadia, Krichen, Saoussen
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2025
Springer Nature B.V
Témata:
Algorithms
Automotive Engineering
Civil Engineering
Computer Imaging
Cost effectiveness
Costs
Customer services
Efficiency
Electric vehicles
Electrical Engineering
Energy consumption
Energy costs
Engineering
Genetic algorithms
Literature reviews
Machine learning
Multiple objective analysis
Optimization
Pattern Recognition and Graphics
Robotics and Automation
Sorting algorithms
User Interfaces and Human Computer Interaction
Vehicle routing
Vision
Battery swapping
NSGA-II
Q-learning
Multi-objective optimization
Electric vehicle routing problem
ISSN:1348-8503, 1868-8659
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Shrnutí:Navigating the challenges of the Electric Vehicle Routing Problem with Battery Swapping Stations (EVRP-BSS), this work is centered on a multi-objective optimization task, simultaneously minimizing battery swap costs and energy consumption costs. Given the intricate nature of this problem and its real- world implications, we propose a particular solution methodology. Our hybridized approach introduces a learn-heuristic that leverages the Non-dominated Sorting Genetic Algorithm II (NSGA II) and the Q-learning algorithm. This method not only addresses the NP-hard complexity of the problem but also aims to improve the sustainability and cost-effectiveness of electric vehicle routing operations. In contributing a fresh perspective to the discourse on efficient and eco-friendly transportation, our study explores novel avenues for sustainable solutions. The experiments showed the good performance of the proposed approach for solving the EVRP-BSS.
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ISSN:1348-8503
1868-8659
DOI:10.1007/s13177-025-00486-9