Cold Chain Logistics Path Optimization with Adaptive Speed and Hybrid Genetic Algorithm Solution

In this work, we aim to adjust vehicle speeds in real time by predicting the surrounding population density based on the spacing of customer locations. We comprehensively consider fixed costs, cargo loss costs, fuel costs, penalty costs, and environmental costs; build a cold chain distribution vehicle path optimization model with the goal of minimizing the total cost and maximizing customer satisfaction; and design a hybrid genetic algorithm solution optimization model. The algorithm dynamically adjusts the tournament scale through the standard deviation of the fitness value, uses the OX cross operator, determines the position of variation based on the customer information matrix, and performs local search optimization with the removal and insertion operators. Through comparison to other algorithms in the literature, the results show that the hybrid genetic algorithm not only improves customer satisfaction, but also maintains a lower total cost, which is obviously superior when solving the complex cold chain distribution path optimization problem; further comparison and analysis of the mathematical model in this paper with the single-dimension satisfaction model reveals that under the same satisfaction constraint threshold, the model in this paper can significantly reduce the system operating cost; we also deeply discuss the influence mechanism of vehicle traveling mode and customer point sparsity radius on distribution path planning.