the 12 DRCMPSP instance sets (totaling 60 instances) in the Multi-Project Scheduling Problem Library (MPSPLIB)

In response to issues such as severe resource conflicts and prominent project delay risks arising from the leapfrog development of the aerospace industry, and considering the real-world scenarios where new projects arrive unpredictably and ongoing project plans change unexpectedly during multi-project execution, this paper proposes the problem of dynamic distributed multi-project resource configuration for aerospace missions. Combining the characteristics of aerospace project tasks and the practical application scenarios of multi-project management, project weights are introduced. A dynamic multi-project resource configuration management process based on Multi-Agent Systems (MAS) and the Critical Chain Project Management (CCPM) method is proposed. A three-stage iterative model is established with the objectives of optimal change scope, shortest single-project duration, and minimal multi-project weighted tardiness. This model comprises reconstruction scope definition, local single-project scheduling, and global multi-project decision-making. A genetic algorithm incorporating optimal inheritance and project weights is designed to solve the model. Based on the DRCMPSP instances from the MPSPLIB benchmark library, a dynamic multi-project resource configuration test set is constructed through parameter configuration, and large-scale numerical experiments are conducted. The results show that the proposed dynamic multi-project resource configuration model and its solution algorithm can effectively mitigate the negative impacts caused by resource conflicts and project delays in aerospace multi-project management. It demonstrates good adaptability to different problem scales and resource utilization coefficients, proving particularly suitable for situations with severe resource conflicts, which aligns with current and future development trends.