Green Cold Chain Logistics Routing Optimization Based on Chaotic Mechanism Hybrid SSA Algorithm With Fuzzy Time Window Multi- Objective Multi-Distribution Center

As the competition in the cold chain logistics industry intensifies, for the problems of high total cost, low customer satisfaction, and low vehicle distribution efficiency in the process of cold chain logistics distribution, this paper takes fuzzy time window and minimum satisfaction as constraints, considers fixed cost, refrigeration cost, fresh goods loss cost, fuel consumption and carbon emission cost, and violation of time window penalty cost in the distribution process, and constructs a model to minimize total distribution cost and maximize customer satisfaction. The multi-objective, multi-distribution center green cold chain logistics path optimization model is constructed to minimize the total distribution cost and maximize customer satisfaction. Based on the SSA algorithm (Sparrow Search Algorithm, SSA), we add tent chaotic mapping to initialize the population, an elite reverse learning strategy to optimize the discoverers in the sparrow population and perform non-dominated ranking, and a firefly perturbation strategy to help the algorithm jump out of the local optimum. By testing three different types of benchmark functions and comparing them with the other four heuristic algorithms, the improved SSA algorithm achieves a better finding effect. Finally, numerical simulation experiments prove the effectiveness of the FISSA algorithm (Improved Firefly-Sparrow Search Algorithm, FISSA)and the rationality of the model in this paper.