Cold Chain Logistics Vehicle Path Optimization Based on Improved Artificial Bee Colony Algorithm

Aims and Background: Cold Chain Logistics is defined as the secure storage, transportation, and handling of temperature-sensitive items from the origin of production to the consumer end. Transport and warehouses with refrigeration systems have been used in this process. Cold chain logistics plays a key role in safely transporting food items to consumers. Objectives and Methodology: In this research article, we have proposed an improved Artificial Bee Colony (ABC) algorithm for the path optimization method of cold chain logistics vehicles. It is difficult to transfer resources across borders, there is no information sharing, and there are geographical restrictions with traditional regional distribution systems. Reaching the consumer end in less time, distance, and expense is made possible in large part by the cold chain logistics vehicle. Data about the flow of traffic on the roads is taken into account when choosing routes. Results and Discussion: Path optimization methods are employed in this study. One of the artificial swarm intelligence algorithms that is based on the functionality of bee colonies is the ABC algorithm. The Cold Chain Logistics (CCL) path is optimized with the aid of Internet technology and Global Positioning System technology. For the delivery of temperature-sensitive items, the most efficient short route is identified. Conclusion: The main aim of this research is to reduce the time, distance, and cost involved in transportation. The algorithm has provided an accuracy of 98.74% in attaining effective transportation of cold logistics and optimal path selection.