Analyzing the optimization of vehicle routing problem with hybrid electric vehicles (VRPHEV) using genetic algorithm approach
DOI:
https://doi.org/10.55573/JISED.118762Keywords:
Vehicle Routing Problem (VRP), Genetic Algorithm (GA), Hybrid Electric Vehicles (HEV), Route Optimization, Sustainable Logistics, Social and Economic DevelopmentAbstract
This project explores the optimization of the Vehicle Routing Problem with Hybrid
Electric Vehicles (VRPHEV) using a Genetic Algorithm (GA) approach to enhance route efficiency and minimize carbon emissions from fossil fuel dependency. The main objective is to calculate the total distance travelled and determine the most optimal delivery sequences that reduce operational logistics expenses and travel times for VRPHEV. The proposed optimization employs Genetic Algorithm operations which are Crossover, Mutation, and Inversion, to generate and evolve high-quality routing solutions from secondary geospatial datasets. Secondary route coordinates were used for the computational analysis, and after 10 structured iterations, significant routing improvements were observed. The total travel distance was reduced from an initial 85,114 meters to an optimized 51,980 meters for Route X (a 38.92% efficiency increase) and from 84,652 meters to 52,600 meters for Route Y (a 37.86 % efficiency increase). Furthermore, the critical battery' consumption constraint of 84,000 meters was successfully maintained across all routing solutions, demonstrating that the optimized paths are fully feasible under VRPHEV physical constraints. These findings indicate that integrating metaheuristic optimization into sustainable transportation operations yields significant strategic, economic, and environmental benefits, supporting social and economic development goals.
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