Research on Data-Driven Fresh Produce Joint Distribution Network Optimization Under Distribution Center Sharing

To address the problem of low efficiency and ineffective utilization of resources in the distribution of fresh produce at the end of the city, and taking into account the seasonal characteristics of the logistics demand of fresh produce. An innovative rolling adjustment framework model based on data-driven for optimizing fresh produce joint distribution network was proposed, which follows seasonal changes. The cycle of the adjustment framework was divided according to seasonal changes, with each cycle including four steps: fresh produce logistics demand prediction, fresh produce joint distribution network optimization, data collection, and parameters adjustment of the prediction model, further to achieve data-driven optimization of joint distribution network for fresh produce. A catastrophe adaptive genetic algorithm with variable neighborhood search (CAGA-VNS) is developed to solve the fresh produce joint distribution network optimization model. Finally, several numerical experiments are conducted to validate the model and algorithm. The results demonstrate that: 1) the rolling adjustment framework model can provide effective fresh produce distribution network optimization decisions when the fresh produce demand changes according to the season changes. 2) The CAGA-VNS algorithm can be more stable with the lowest difference percentage being 9.16%. 3) distribution center sharing strategy can effectively improve utilization of resources, reduce the total distribution cost of 19.46% and save travel distance of 14.72%.