Robust scheduling strategies for collaborative human-UAV missions

Resource allocation in collaborative human-UAV missions has become an important research area in recent years. Traditional deterministic strategies for task scheduling, such as job-shop schemes, can lead to poor performance, since these strategies fail to account for human cognitive requirements or behavioral uncertainty. In response, we present a flexible mixed-integer linear programming framework that can potentially address both of these issues in finite horizon scheduling applications. Specifically, we illustrate how cognitive workload constraints can be formulated as a mixed-integer linear program, and introduce robustness to uncertain processing times through the use of scenarios. We explore the modularity and utility of this simple framework by introducing additional layers of complexity, including receding horizon planning and adaptive estimation. Throughout the discussion, we use simulation studies to discuss the functionality of these algorithms, as well as various issues regarding practical implementation.