Dynamic Programming of Complex Tasks Based on Regretting Greedy Algorithm

This paper presents a task planning and resource scheduling algorithm based on a regret-driven greedy strategy and dynamic programming. The algorithm addresses key challenges such as resource conflicts between tasks, limited adaptability to dynamic task arrivals, and the trade-off between task benefits and costs. By incorporating real-time task evaluation, dynamic resource allocation and release mechanisms, and task interruption and insertion strategies, the algorithm achieves global optimization in complex and dynamic environments. The core regret mechanism allows lower-benefit tasks to release resources under constrained conditions, prioritizing higher-benefit tasks to maximize overall gains. Experimental results demonstrate that the proposed method excels in resource utilization, task completion rate, and total benefit optimization, providing a robust solution for task optimization under resource-limited scenarios.