electric vehicle routing problem

Imitation Learning via Deep Q-Learning for Multi-robot Path Planning (2026)

Hybrid Artificial Intelligence Systems Authors Anikó Kopacz, Enol García González, Camelia Chira, José R. Villar Abstract Multi-robot path planning is essential for industrial operations, e.g., for manufacturing and warehouse logistics. Traditional path planning approaches may impose an unnecessary increase in computational demands when countered by unexpected events or failures. Thus, investigating machine learning approaches,…

A Granular Ball–Ant Colony Optimization Framework Enhanced by Variable Neighborhood Search for the Multi-Depot Half-Open Time-Dependent EVRP (2026)

Annual Conference on Genetic and Evolutionary Computation Authors Yingkai Xu, Anikó Kopacz, Camelia Chira Abstract The widespread adoption of electric vehicles (EVs) in urban logistics has led to the Electric Vehicle Routing Problem (EVRP) emerging as a prominent topic in combinatorial optimization research. To improve the efficiency of urban logistics distribution and minimize operational…

A Hybrid Granular Ball-Ant Colony Optimization for the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (2025)

Electric vehicles (EVs) are increasingly utilized in logistics and distribution to expedite achieving carbon peaking and neutrality goals, drawing considerable attention to the Electric Vehicle Routing Problem (EVRP). This study investigates the Multi-Depot Half-Open Time-Dependent Electric Vehicle Routing Problem (MDHOTDEVRP) and aims to improve coordination and distribution efficiency among logistics depots. This problem involves…