Multi-Task Scheduling Optimization Method Integrating Visibility Graph, Simulated Annealing, and A* Algorithm

This paper presents a multi-task allocation method integrating the visibility graph, A* algorithm, and simulated annealing to enhance scheduling stability and path accuracy in complex environments. The method constructs a path cost matrix and globally optimizes task sequences. Experimental results show a 2% reduction in path length compared to Euclidean-based methods while improving path planning accuracy, allocation efficiency, and stability, providing a robust solution for dynamic multi-task scheduling.