Multi-objective distribution path optimization of cold chain logistics based on improved grey wolf optimizer

In response to the “Dual Carbon” strategy, optimizing delivery routes is crucial for enhancing the efficiency of cold chain logistics enterprises. However, existing studies often simplify multi-objective conflicts into single-objective problems, and general optimization algorithms have limitations when solving discrete routing problems. To address this, this study proposes a multi-objective delivery route optimization method for cold chain logistics based on the Improved Grey Wolf Optimizer (GWO). This method introduces a discrete mechanism to address the route encoding problem, combines the Particle Swarm Optimization (PSO) algorithm to accelerate convergence in the later stages, and employs a linear crossover operation to enhance population diversity. This study constructs a three-objective model aimed at minimizing total cost, minimizing the number of time-window violations, and minimizing carbon emissions. Based on this, the study proposes the Multi-objective Grey Wolf Optimizer (Mo-GWO), which combines discrete encoding, hybrid initialization, diversity archiving, and hybrid search strategies, to solve the problem. Experimental results show that, compared to the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, the proposed Mo-GWO algorithm achieves an average 15.2% improvement in the supervolume metric of the Pareto solution set. Meanwhile, the total delivery cost is reduced by 8.9%, and the average vehicle loading rate increases to 85.7%. In the ablation experiments, the delete-insert operator had the most significant impact on algorithm performance; removing this module resulted in a total cost increase of 339 yuan. Therefore, this method can effectively improve cold chain logistics delivery efficiency, reduce enterprise operating costs, and decrease pollutant emissions.