Using Adaptive Priority Weighting to Direct Search in Probabilistic Scheduling

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Many scheduling problems reside in uncertain and dynamic environments -- tasks have a nonzero probability of failure and may need to be rescheduled. In these cases, an optimized solution for a short-term time horizon may have a detrimental impact over a broader time scale. We examine a scheduling domain in which time and energy on a phased array radar system is allocated to track objects in orbit around the earth. This domain requires probabilistic modeling to optimize the expected number of successful tasks on a particular day. Failed tasks must be attempted again on subsequent days. Given a set of task requests, we study two long-term objectives: percentage of requests initially successful, and the average time between successful request updates. We investigate adaptive priority weighting strategies that directly influence the short-term objective function and thus indirectly influence the long-term goals. We find that adapting priority weights based on when individual tasks succeed or fail allows a catalog of requests to be filled more quickly. Furthermore, with adaptive priorities, we observe a Pareto-front effect between the two long-term objectives as we modify how priorities are weighted, but an inverse effect of weighting when the priorities are not adapted.