Robust Task Scheduling for Multi-Operator Supervisory Control Missions

Abstract

In future collaborative missions involving humans and unmanned aerial vehicles, especially those involving multiple human operators, the issue of resource allocation will be a crucial component to system success. Traditional deterministic strategies for task allocation and scheduling, such as those designed for job-shop applications, can often lead to poor performance in human-centered systems because these strategies fail to account for operator cognitive requirements or for the large amounts of uncertainty in human behavior. In light of this, a flexible framework that can potentially address both of these issues in finite horizon scheduling applications involving multiple operators is presented. Specifically, operator task load constraints are included as a part of a mixed integer program-based scheduling framework, which also incorporates robustness to uncertain processing times through the use of scenarios. The utility and modularity of this framework is then explored through the introduction of adaptive components that can further mitigate uncertainty and potentially boost performance. Finally, a heuristic, auction-based strategy is proposed for task allocation to reduce computation time to reasonable levels. Throughout the discussion, numerical examples are used to discuss the functionality of these algorithms, and various considerations for future practical implementation are discussed.