Flexible Logistics Sorting System Allocation via Hybrid GA-PSO with Dynamic Clustering

To address the challenges posed by the large sorting volume of cigarettes in tobacco logistics distribution centers and the significant impact of cigarette specification allocation on the overall processing time of orders, this study aims to optimize the allocation of each sorting zone and enhance sorting efficiency. A mathematical model is developed with the objective of minimizing the similarity coefficients of specifications within each zone, which is then solved using an enhanced genetic particle swarm dynamic clustering (GA-PSO-K) algorithm. Initially, the similarity coefficient of each specification is improved by incorporating the sorting quantity of each specification into the fitness function. Subsequently, the inertia weight factor in the particle swarm algorithm is adaptively adjusted to enable its dynamic variation. Finally, cross-variance is introduced into the genetic algorithm to expand the solution search space, and the results are compared with those of other algorithms using Matlab. The algorithm’s performance is simulated and validated in an EM-plant environment. In the context of data simulation verification at a tobacco logistics distribution center, the processing time for order handling with the GA-PSO-K algorithm was reduced to 234.5 seconds, significantly outperforming traditional methods and effectively improving the efficiency of flexible logistics sorting. This algorithm leverages the advantages of both particle swarm optimization and genetic algorithms, demonstrating improved convergence and solution quality, thus offering a novel approach for flexible logistics product specification allocation.