Optimizing fresh agricultural product distribution paths under demand uncertainty: A particle swarm optimization-based algorithm

Consumers' demand for fresh agricultural products (FAPs) and their quality requirements are increasing in the current agricultural-product consumption market. FAPs' unique perishability and short shelf-life features mean a high level of delivery efficiency is required to ensure their freshness and quality. However, consumers' demand for FAPs is contingent and geographically dispersed. Therefore, the conflicting relationship between the costs associated with the logistics distribution and the level of delivery quality is important to consider. In this paper, the authors consider a fresh agricultural-product distribution path planning problem with time windows (FAPDPPPTW). To address the FAPDPPPTW under demand uncertainty, a mixed-integer linear programming model based on robust optimization is proposed. Moreover, a particle swarm optimization algorithm combined with a variable neighborhood search is designed to solve the proposed mathematical model. The numerical experiment results show the robustness and fast convergence of the algorithm.