The practical engineering of satellite tracking telemetry and command (TT&C) is often disturbed by unpredictable external factors, including the temporary rise in a significant quantity of satellite TT&C tasks, temporary failures and failures of some TT&C resources, and so on. To improve the adaptability and robustness of satellite TT&C systems when faced with uncertain dynamic disturbances, a hierarchical disturbance propagation mechanism and an improved contract network dynamic scheduling method for satellite TT&C resources were designed to address the dynamic scheduling problem of satellite TT&C resources. Firstly, the characteristics of the dynamic scheduling problem of satellite TT&C resources are analyzed, and a mathematical model is established with the weighted optimization objectives of maximizing the revenue from task completion and minimizing the degree of plan disturbance. Then, a bottom-up distributed dynamic collaborative scheduling framework for satellite TT&C resources is proposed, which includes a task layer, a resource layer, a central internal collaboration layer, and a central external collaboration layer. Dynamic disturbances are propagated layer by layer from the task layer to the central external collaboration layer in a bottom-up manner, using efficient heuristic strategies in the task layer and the resource layer, respectively. We use improved contract network algorithms in the center internal collaboration layer and the center external collaboration layer, the original scheduling plan is quickly adjusted to minimize the impact of disturbances while effectively completing dynamic task requirements. Finally, a large number of simulation experiments were carried out and compared with various comparative algorithms. The results show that the proposed algorithm can effectively improve the solution effect of satellite TT&C resource dynamic scheduling problems, and has good application prospects.

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution.