Optimisation Methods for Cold Chain Logistics Path Considering Carbon Emission Costs in Time-Varying Networks

With the escalating global climate change, the cost of carbon emissions has become a crucial metric for evaluating the sustainability of logistics systems. This study addresses the optimisation of cold chain logistics routes in a time-varying network environment, considering the carbon emission cost factor, and proposes an enhanced particle swarm optimisation algorithm to solve this optimisation problem. Firstly, we establish a cold chain logistics optimisation model that incorporates the time-varying network, integrating logistics route planning with carbon emission costs. Subsequently, we design an improved particle swarm optimisation algorithm suitable for time-varying networks. This algorithm optimises vehicle routes and adjusts delivery times to minimise the total cost incurred during distribution. Finally, through simulation experiments, we analyse the impact of vehicle speeds and carbon trading mechanisms on optimisation outcomes. The results demonstrate that this method effectively optimises cold chain logistics routes, considering real network conditions and environmental factors, thereby reducing delivery costs and carbon emissions.