Optimization of Multimodal Transportation Routes for Refrigerated Goods Under Uncertain Demand

With rising customer demands for the timeliness and quality of refrigerated goods, the efficiency and fluidity of cold chain logistics remain inadequate, resulting in a notable imbalance between supply and demand in the cold chain market. To reduce the damage of fresh produce and lower logistics costs, this paper introduces multimodal transportation into the cold chain market and performs an analysis of optimizing multimodal transportation routes for refrigerated goods. This study constructs a mixed-integer programming model for cold chain multimodal transportation, aiming to minimize total costs while considering carbon emissions and uncertain demand. An improved adaptive large neighborhood search (ALNS) algorithm is developed to solve the mathematical model, featuring improved adaptive scoring and operator selection mechanisms. The algorithm’s performance is validated through a real-world multimodal transportation network in China. Furthermore, a sensitivity analysis is performed on rail freight rates, confidence levels, and ambient temperature, from which we derive managerial insights with practical significance.