In the reverse logistics recycling process, considering the diversity of recycling types, a variety of vehicle models are used to meet the reverse logistics recycling requirements. Therefore, this paper considers the fixed costs, transportation costs, carbon emission costs during driving, and time penalty costs in the reverse logistics process under multiple constraints such as multiple vehicle types and time windows in the recycling center. The recovery point is the recovery model, and an improved genetic algorithm is used to solve it. Drawing lessons from the idea of greed, the superiority of the initial population is improved; the entry matrix and the exit matrix of the iterative population are constructed, and the crossover operator is improved based on this, and the forward insertion method is introduced to design the hybrid crossover operation to speed up the population. At the same time, the improvement of the mutation operator is proposed to increase the diversity of the population. The experimental results show that the multivehicle mixed delivery mode can reduce costs more effectively than single-vehicle models, and the improved genetic algorithm has better convergence and stability.
Reverse Logistics Vehicle Routing Optimization Problem Based on Multivehicle Recycling
