Dynamic distribution route optimization for fresh products considering timeliness demand

ObjectiveThis study established distribution route optimization model for fresh products with the goal of minimizing distribution cost, due to the characteristics of high dynamic purchasing orders and high distribution frequency for online fresh products. The model considered instant delivery, service time window, product freshness and traffic congestion.MethodThe random operators and greedy operators were freely combined to insert and remove. The optimization method with local search was adopted to transform the distribution route dynamic variation problem into multiple traveling salesman problem. An improved adaptive large neighborhood search algorithm was designed to solve the model. Simulation tests were conducted through standard test cases and real data.ResultThe improved algorithm accelerates convergence while maintaining solution quality. By analyzing customer distribution patterns and fresh product timeliness constraints, it strategically avoids low-speed driving to achieve rapid distribution. Compared with classical adaptive large neighborhood search, the improved algorithm reduces computational time by at least 20.8% and increases the optimization objective value by at least 10.3%, demonstrating excellent convergence and search capabilities.ConclusionCombined with the actual phenomenon of fresh product dynamic distribution for supermarkets, the influence of adjusting decay rate and cost composition of sample cases on the objective was obtained. The distribution route optimization results in different scenarios were obtained, which verified the effectiveness and practicality of the improved algorithm.