| Abstract |
To overcome the deficiencies of the reverse logistics vehicle routing optimization study in the reasonable combination of vehicle sharing and multi-frequency recovery under smart recovery modes, a reverse logistics vehicle routing optimization scheme based on resource sharing and multi-frequency recovery was proposed. A bi-objective optimization model including the minimum reverse logistics operating costs and the minimum number of vehicles was established, and the logistics operating costs contained the vehicle transportation cost, the vehicle lease and maintenance cost, disposed cost of recycled products, the penalty cost of the time window violation and environmental externality benefit. A two-stage Clarke-Wright Self-learning Non-dominated Sorting Genetic Algorithm-II (CW-SLNSGA- n) was designed to solve the model. In the first stage of the algorithm, a Clarke-Wright (CW) savings algorithm and a sweep algorithm were combined to improve the quality of the initial solutions. In the second stage, a self-learning mechanism was embedded into the non-dominated sorting genetic algorithm-II (NSGA-II) • and then the occurrence probabilities of the mutation and crossover could vary according to the change of the fitness value. In addition, an elite iteration strategy was used to retain the individuals with better fitness value, which improved the search performance of the algorithm. The effectiveness of the algorithm was verified through the comparative analysis with the Multi-objective Ant Colony Optimization (MOACO). Multi-objective Whale Optimization Algorithm (MOWOA) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEAD). Finally, the proposed method was verified via a case study, and the ablation experiment of the proposed algorithm was conducted by combining the elite iteration strategy and the self-learning mechanism, the number of recycled vehicles and the operating costs of the reverse logistics were analyzed and discussed when the recycling center selected different capacities of recycled vehicles. The results showed that the proposed model and algorithm could reduce the operating costs of the reverse logistics and the number of vehicles, realize multi-frequency recovery and vehicle sharing scheduling effectively. so as to provide the decision reference and method support for the construction of the reverse logistics network and the smart city under smart recovery modes. © 2025 CIMS. All rights reserved. |
