Cost optimization model design of fresh food cold chain system in the context of big data

Abstract

The assessment of cold chain logistics for fresh products can be more precise with high-dimensional information data, providing valuable insights for the optimization of associated costs. Nonetheless, traditional data processing techniques fail to meet the processing efficiency required for such high-dimensional cold chain logistics data. Therefore, this paper proposes a spectral clustering algorithm based on the local standard deviation and optimized initial center, which comprehensively analyzes the fixed, transportation, refrigeration, and cargo damage costs of cold chain logistics. Additionally, this algorithm includes a variation operator based on clustering and introduces a large neighborhood search mechanism for optimizing the individual connectivity gene layer after selecting the gene layer site for variation. Simulation results demonstrate that the proposed algorithm exhibits better convergence in 15 iterations, reduces error rates, and significantly cuts down on the clustering process time. This ultimately leads to a reduction in the total cost of cold chain calculation.