Abstract
The rapid growth of global air cargo demand has intensified the need for more efficient and intelligent storage allocation within airport cargo terminals. Traditional static allocation and rule-based systems struggle to adapt to dynamic cargo flows, leading to operational inefficiencies. This study aims to develop a hybrid decision support system that optimizes storage allocation by integrating machine learning and multicriteria decision-making techniques. A Random Forest Classifier was trained using historical cargo data, including weight, quantity, size, priority, and cargo type, to predict optimal storage zones. To enhance interpretability and expert control, the Analytic Hierarchy Process AHP was used to derive feature weights, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to rank the best storage alternatives. The model was tested on real-world data from Almaty International Airport involving 1,500 cargo orders. Results show that the system reduced average storage time by 14%, minimized cargo redistributions by 22%, increased storage density by 9%, and improved on-time delivery for priority cargo by 17% compared to traditional FIFO-based methods. The integration of data-driven learning with expert judgment offers a robust and transparent decision-making framework. These improvements confirm the value of combining machine learning with AHP–TOPSIS methods in logistics operations. This system presents significant implications for airport terminal managers seeking to enhance operational throughput, academic researchers exploring hybrid intelligent systems, and policymakers promoting digital logistics infrastructure. Future studies may include adaptive learning, seasonal cargo flow modelling, and digital twin-based scenario testing to further generalize the solution.