A Hybrid Genetic-Neural Optimization Algorithm for Cold Chain Logistics Based on Multivariate Data Mining

With the development of global trade and the improvement of consumers’ requirements for the quality of fresh products, the optimization of the cold chain logistics supply chain is imminent, while the traditional cold chain is difficult to meet modern needs due to problems such as information islands, resource waste, and low efficiency. This study proposes an intelligent optimization algorithm that integrates adaptive elite retention genetic algorithm and multilayer perceptron (MLP) to improve the efficiency and service quality of cold chain logistics with the help of multivariate data mining. The genetic algorithm uses hybrid coding to optimize the multi-objective of transportation routes and inventory parameters, combined with roulette selection and elite retention mechanism. MLP constructs a multi-input and multiple-output model, processes 8-dimensional features at the input layer, extracts nonlinear correlation through two hidden layers, outputs the optimization target, and uses Adam optimizer, weighted mean square error loss function and early stop mechanism, genetic algorithm population size 100, crossover probability 0.8.The experiment integrates 100,000 real operation data (80,000 training, 10,000 verification, and 10,000 tests) of a fresh food e-commerce company, covering environmental and operational variables, synthesizes 50,000 extreme scene data with GAN, and builds a noisy scene simulation system based on the SimPy library, relying on Intel i7 - 12700K CPU and NVIDIA RTX 3060 GPU. Compared with the traditional solution, the algorithm reduces transportation costs by 15.2% (from 12.5 yuan/order to 10.6 yuan/order), increases delivery time by 20.7% (from 58 minutes to 46 minutes), reduces temperature anomaly rate by 70% (from 5.0% to 1.5%), and provides a feasible solution for intelligent cold chain logistics combined with real-time Internet of Things and edge computing technology to promote the sustainable development of the supply chain.