The development of intelligent warehouse systems and the automation of logistics processes require effective solutions for task allocation in heterogeneous multi-robot complexes, particularly in the cooperative transportation of large and heavy cargo. The aim of this work is to develop and verify a hybrid model for cooperative transportation task (CTT) identification and allocation in a warehouse environment, taking into account multi-criteria optimization. A brief review of publications on the application of mivar technologies and machine learning methods in the mathematical modeling of complex robotic systems is provided. A two-level approach is proposed, including a mivar decision-making system for the automatic identification of CTT and a task allocation model based on a combined auction algorithm. The required number of transport robots (RT) is determined by the mivar decision-making system, considering the size and mass of the cargo. The developed mathematical model for CTT allocation aims to improve efficiency and reliability by accounting for key dynamic factors (heterogeneity, redundancy, and path-dependent costs). Simulation experiments with 30 transport robots and 100 tasks demonstrated the superiority of the proposed method over baseline strategies (Random, Nearest Neighbor, Greedy Capacity): when processing 6 CTT, the total cost reduction reached up to 40.7%, and with 12 tasks, an additional reduction of 10.8% was achieved while maintaining 100% success rate. The model’s ability to scale efficiently was established, manifesting in an additional cost reduction of 10.8% as the number of tasks increased. The results indicate the robustness, adaptability, and high practical applicability of the model for integration into modern intelligent warehouse systems that handle a diversified range of cargo
