An improved genetic algorithm for the hybrid electric vehicle routing problem with mode selection

作者：W. Wijesekara, Z. Juman · 发表于：Ceylon Journal of Science · 年份：2025 · DOI：10.4038/cjs.v54i2.8224

Due to the implementation of stricter regulations on carbon emissions worldwide, the use of hybrid electric vehicles (HEVs) is increasing rapidly. However, optimizing their routing while considering energy efficiency remains a critical challenge. The limited battery capacity is the main disadvantage of these vehicles, necessitating stops at recharging stations during transportation. Accordingly, it is essential to incorporate these factors when planning routes to minimize energy consumption. In this study, we examined the Hybrid Electric Vehicle Routing Problem (HEVRP) with mode selection, where vehicles can dynamically switch between multiple driving modes (batterybased, gasoline-based, balance, and only gasoline) based on road conditions to enhance fuel efficiency. We present an Improved Genetic Algorithm (IGA) that integrates the Genetic Algorithm (GA) with a Local Search (LS) method to solve this problem, which is formulated as a mixed-integer linear programming model. A comparative assessment between the proposed IGA and a standard GA demonstrates the improved efficiency and effectiveness of our approach. This study contributes by introducing an optimized mode-selection strategy, validating our approach with benchmark data, and demonstrating the potential for reduced energy consumption in logistics.