Optimization of Low Carbon Medicine Cold Chain Delivery Path Based on Adaptive Improved Ant Colony Algorithm

Aiming at the problem of incomplete consideration of comprehensive distribution cost in the current cold chain distribution path optimization, according to the characteristics of pharmaceutical cold chain distribution, considering transportation cost, cargo damage cost, carbon emission cost, refrigeration cost and time window constraints, a path optimization model with the goal of minimizing comprehensive distribution cost was established, and an adaptive improved ant colony algorithm was proposed. Firstly, a dynamic selection factor is introduced to adaptively update the selection ratio, and the time window parameter and distance function are introduced to improve the state transition rule. Secondly, the Metropolis criterion in simulated annealing algorithm is used to filter the solutions. Finally, an adaptive pheromone evaporation factor is defined to improve the global performance of the algorithm and the convergence speed of the algorithm. Simulation experiments verify the effectiveness of the optimization model and improved algorithm in this paper, which has certain significance for the research on the optimization problem of pharmaceutical cold chain distribution path under the concept of low carbon sustainable development, and helps the cold chain transportation industry to transition to low carbon economy.