Partial automation means that human schedulers can participate in incremental scheduling decisions. Algorithms from operations research and most heuristic search techniques involve humans in the problem set up but not in the generation of schedules. These algorithms work well when the problem is modeled perfectly and is * This work was partially supported by the Defense Advanced Research Projects Agency (DARPA) under contract DAAH01-90-0080 and partially supported by IR&D funding frcxn Advanced De,ion Systems.
We consider the problem of constructing abstract representations for planning in high-dimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner.
Project scheduling is a common business management task. However, current business management environment has become more open and dynamic, which jeopardizes the effectiveness of the traditional approaches. In this abstract, I summarize my works in addressing two variations of project scheduling problems, including a combinatorial auction based approach for solving the decentralized multi-project scheduling problem, and a sampling based approach for solving the problem of project scheduling under time-dependent duration uncertainties.
The generation of executable schedules for space-based observatories is a challenging class of problems for the planning and scheduling community. Existing and planned space-based observatories vary in structure and nature, from very complex and general purpose, like the Hubble Space Telescope (HST), to small and targeted to a specific scientific program, like the Submillimeter Wave Astronomy Satellite (SWAS). However the fact that they share several classes of operating constraints (periodic loss of target visibility, limited onboard resources, like battery charge and data storage, etc.) suggests the possibility of a common approach. The complexity of the problem stems from two sources. First, they display the difficulty of classical scheduling problems: optimization of objectives relating to overall system performance (e.g., maximizing return of science data), while satisfying all constraints imposed by the observation programs (e.g., precedence and temporal separation among observations) and by the limitations on the availability of capacity (e.g., observations requiring different targets cannot be executed simultaneously).
MAOS-FSP  is a multiagent optimization system (MAOS) for solving the Flowshop Scheduling Problem (FSP). MAOS-FSP shares the MAOS kernel with other MAOS applications (e.g. MAOS-GCP and MAOS-TSP), and contains some modules that are specifically for tacking FSP. Related Information: Please find other related code and software in our Source Code Library. License information: MAOS-FSP is free software; you can redistribute it and/or modify it under the Creative Commons Non-Commercial License 3.0.