Research on optimization of new energy logistics vehicle distribution routing based on improved genetic algorithm

Abstract

Under the background of the “dual-carbon” strategy, new energy logistics vehicles have become increasingly important in urban distribution systems due to their environmental friendliness and low operating emissions. However, compared with conventional fuel vehicles, their routing and scheduling are more strongly constrained by battery capacity, driving range, and charging requirements, which makes the route optimization problem considerably more complex. Focusing on the distribution scenario of new energy logistics vehicles, this paper establishes a route optimization model by comprehensively considering delivery distance, energy consumption, and operating cost. On this basis, an improved genetic algorithm (IGA) is proposed to solve the problem efficiently. The proposed method incorporates heuristic initialization, adaptive crossover and mutation mechanisms, route repair, and 2-opt local search, so as to enhance both global exploration and local exploitation. To verify the effectiveness of the proposed approach, comparative experiments are conducted against the standard genetic algorithm (GA), tabu search (TS), and ant colony optimization (ACO). In addition, a real-world enterprise case is employed to evaluate the engineering applicability of the model and algorithm. Experimental results show that the proposed IGA outperforms the comparison algorithms in terms of route length, total energy consumption, return-to-depot frequency, feasible solution ratio, and convergence efficiency. The real-case study further demonstrates that the proposed approach can significantly reduce total delivery cost and delivery interruption rate. The findings indicate that the proposed method provides an effective and practical optimization tool for intelligent dispatching and green logistics distribution using new energy vehicles.