Optimization of Multi-Temperature Co-Transmission Paths under Time-Varying Road Networks

This paper addresses the diversified needs of urban cold chain distribution and proposes innovative solutions based on storage type multi-temperature co-distribution and mechanical type multi-temperature co-distribution modes. We present an electric vehicle path optimization model aimed at minimizing total costs, taking into account time-varying speed in accordance with urban traffic patterns. Additionally, a genetic algorithm is designed to solve the multi-temperature co-matching optimization path. The study's results reveal that the storage type multi-temperature co-distribution transport mode offers superior economic efficiency, product security, safety, and resource utilization. By comparing and analyzing the results of model solving under different battery capacities, loads, and distributions speeds, the total cost of distribution is optimal when the battery capacity is 120 kWh, the maximum load is 100 kg, and the normal driving speed is 60 km/h. The mechanical multi-temperature co-distribution mode is optimal for the total cost of distribution at a battery capacity of 100 kWh, a maximum load of 100 kg, and a normal driving speed of 50 km/h. The study aims to provide reference significance for logistics companies when making route selection.