Chance-constrained Static Schedules for Temporally Probabilistic Plans

Time management under uncertainty is essential to large scale projects. From space exploration industrial production, there a need schedule and perform activities. given complex specifications on timing. In order generate schedules that are robust in the duration of activities, prior work has focused problem framing uses an interval-bounded representation. However, such approaches unable take advantage known probability distributions over duration. this paper we concentrate probabilistic formulation temporal problems with uncertain duration, called simple problem. As often have unbounded range outcomes, consider chance-constrained solutions, guarantees meeting constraints. By considering able use risk as resource, reason relative likelihood derive higher utility solutions. We first demonstrate our approach by encoding convex program. then develop more efficient hybrid algorithm whose parent solver generates allocations child for particular allocation. The made leveraging existing scheduling algorithms, while extracting conflicts allocations. numerical experiments show advantages reasoning uncertainty, comparing generated allocation against those from bounded uncertainty. also empirically solution time greatly reduced incorporating conflict-directed