This study introduces ACO-NSGAII, a hybrid metaheuristic algorithm that integrates ant colony optimization (ACO) and non-dominated sorting genetic algorithm II (NSGA-II), to address the bi-objective Electric Vehicle Routing Problem with Time Windows (EVRPTW). The algorithm aims to minimize both the overall travel distances and the number of vehicles required in last-mile delivery. Our approach starts with ACO to generate an initial solution focusing on distance mini-mization, followed by NSGA-II to optimize the dual objectives efficiently. Extensive computational experiments demonstrate that ACO-NSGAII significantly outperforms existing methods like Random-NSGAII and NN-NSGAII (an integration of near-est neighbor and NSGA-II), offering promising solutions for sustainable urban logistics. The findings contribute valuable insights into the trade-offs between minimizing distance and vehicle usage. Moreover, our research extensively studies the effectiveness of ACO-NSGAII for complex routing problems and highlights the impact of search budget allocation on the convergence of ACO-NSGAII.
ACO-NSGAII: A Novel Metaheuristics for Bi-Objective Electric Vehicle Routing Problems
2024-09-24
667876 byte
Conference paper
Electronic Resource
English
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