Hybrid vehicle distribution optimization considering time-dependent road network

This study focuses on the hybrid fleet vehicle routing problem under time-dependent road networks. The proposed model comprehensively considers the influence of vehicle speed on travel time and vehicle load on energy consumption, with the objective of minimizing the total cost, including dispatch and energy consumption costs. The regional distribution centers are selected using the Density K-means algorithm, and a neighborhood search genetic algorithm is designed to solve the routing optimization model. Comparative experiments with the Gurobi solver on instances of different scales demonstrate that the proposed algorithm performs well in both solution quality and computational efficiency. In addition, the Solomon benchmark instance RC101 is solved, with results showing a total delivery distance of 1222.7, a total cost of 3334.57, and a fleet of 19 vehicles, further validating the effectiveness and practicality of the proposed model in hybrid fleet delivery optimization.