A performance-impact based multi-task distributed scheduling algorithm with task removal inference and deadlock avoidance

Multi-task distributed scheduling (MTDS) remains a challenging problem for multi-agent systems used for uncertain and dynamic real-world tasks such as search-and-rescue. The Performance Impact (PI) algorithm is an excellent solution for MTDS, but it suffers from the problem of non-convergence that it may fall into an infinite cycle of exchanging the same task. In this paper, we improve the PI algorithm through the integration of a task removal inference strategy and a deadlock avoidance mechanism. Specifically, the task removal inference strategy results in better exploration performance than the original PI, improving the suboptimal solutions caused by the heuristics for local task selection as done in PI. In addition, we design a deadlock avoidance mechanism that limits the number of times of removing the same task and isolating consecutive inclusions of the same task. Therefore, it guarantees the convergence of the MTDS algorithm. We demonstrate the advantage of the proposed algorithm over the original PI algorithm through Monte Carlo simulation of the search-and-rescue task. The results show that the proposed algorithm can obtain a lower average time cost and the highest total allocation number.