Research on Anti-interference Management of Cold Fresh Food Distribution Based on Prospect Theory and Adaptive PSO Algorithm

Abstract

Abstract This paper addresses the challenges faced by fresh frozen product supply enterprises, where the original distribution plans become infeasible due to disturbances such as refrigeration equipment failures during the product delivery process. These disturbances may lead to potential damage to the enterprise's brand reputation, a decrease in customer satisfaction, and economic losses. The research delves into these issues and proposes solutions. Firstly, concerning the supplier delivery mode, a mathematical model is proposed based on the Prospective Theory, suitable for evaluating multiple factors such as supplier brand, customer satisfaction, and supplier economic benefits. Secondly, building upon this model, a combined function model is generated by integrating supplier brand reputation and supplier economic benefits. This model simultaneously considers the relationships between supplier brand reputation, customer satisfaction, and supplier economic benefits in order of priority. Thirdly, to find the optimal solution for this model, a corresponding PSO (Particle Swarm Optimization) particle model is designed, aligning the delivery plan with the particle's position coordinates. Fourthly, by enhancing the PSO algorithm, the iteration count of the algorithm is significantly optimized, enabling intelligent termination of the iterative loop exploring the optimal solution, thereby improving the efficiency of solving the problem. Validation on multiple Solomon instance datasets demonstrates the superior performance of the proposed algorithm. The solutions it provides in the event of frozen equipment failures are more conducive to maintaining the supplier's corporate brand image, enhancing customer satisfaction, and boosting economic profits for the enterprise.