Optimization of Collaborative Model of Cold Chain Logistics Transportation and Inventory Based on ACO-KELM

Abstract

With the increasing demand for cold chain logistics for food, medicine, and other industries, how to improve the transportation efficiency and inventory management level of cold chain logistics has become a research hotspot. This paper proposes a collaborative optimization model of cold chain logistics transportation and inventory based on an ant colony optimization algorithm (ACO) and Kernel Extreme Learning Machine (KELM). The core of this model is to combine transportation route optimization with the forecasting function of inventory management, optimize the transportation route through ACO, and use KELM to accurately forecast inventory demand to realize the dual optimization of transportation and inventory. The comprehensive optimization of transportation routes, inventory holding cost, out-of-stock rate, and other objectives are considered by establishing the collaborative optimization objective function of the cold chain logistics system. Then, combined with the characteristics of the ant colony algorithm and the KELM model, a collaborative fusion mechanism is proposed, and the interactive feedback process of path planning and inventory forecasting is optimized. The total cost of transportation route optimization is 58.74, indicating that cold chain logistics costs are relatively high under the current scheme. At the same time, in terms of inventory management, the inventory holding cost is 21.56, indicating that the inventory cost is relatively controllable. With the further improvement of path optimization, the transportation cost can be reduced to 87.39, while the inventory out-of-stock rate remains at 12.02. The low out-of-stock rate effectively ensures the stability of logistics services. After careful consideration, route selection and inventory management coordination further optimize energy consumption and guarantee efficient operation of cold chain logistics system. The transportation energy consumption is 45.63, while the system's overall energy efficiency is increased to 92.84, indicating that the energy consumption management of the cold chain logistics system has been effectively improved, and collaborative optimization of route and inventory helps improve overall operational efficiency.