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Q uic kT im e™ an d a T IFF ( Un co m pr e ss e d) d ec o mp r es s or a re n ee d ed to s e e th is p ictu r e. ADASS the Planning and Scheduling Perspective Roadmap: - How planning and scheduling fits in at ADASS - ADASS planning and scheduling posters and presentations - Invited talk I gave on scheduling research I am doing with Mark Johnston Q uic kT im e™ an d a T IFF ( Un co m pr e ss e d) d ec o mp r es s or a re n ee d ed to s e e th is p ictu r e. Planning and Scheduling • ADASS = Astronomical Data Analysis and Software Systems – Main focus of the conference is on post observation data handling and processing – How does planning and scheduling fit in? • Many of the people who attend ADASS operate telescopes – Realize that planning and scheduling impacts their lives • Lots of people were interested in planning and scheduling issues. – ADASS = Astronomical (Data Analysis and Software Systems) • ADASS planning and scheduling papers give the “ • ” as opposed to the “state of the art”. – Papers describe what missions are doing as opposed to the latest developments in planning and scheduling. – For the state of the art in planning and scheduling try the International Conference on Automated Planning and Scheduling (ICAPS) or IWPSS Posters and Presentations • The GBT Dynamic Scheduling System: A New Scheduling Paradigm – Ground based system that dynamically schedules observers a few days in advance based on long term constraints and predicted weather – Papers discussed how this worked with users and technical approaches (e.g. knapsack problem) • Planning and Executing Airborne Astronomy Missions on SOFIA – Telescope mounted on the side of an airplane – Have worked on scheduling techniques for individual missions – Interested in long range planning techniques • Mission Science Operations at the Southwest Research Institute in Boulder, Colorado • Planning and scheduling within the WSO-UV observatory – How to adapt an existing plan – Mixed mode automated planning and realtime operations Invited Talk Summary • Research in multi-objective multi-participant scheduling. multi-objective optimizer • science and • Goal: to increase science resource optimized schedules return • Provide tools which allow • objectives • constraints operators to make tradeoffs • alternatives • compromises • tradeoffs between competing • decisions • optimized objectives schedules • Work done with Mark Johnston at JPL missions multiple participants enable participants to work Tools do not support collaboration wrong Cartoon Example of what can go between participants togetherscheduling process. in the Scheduler Scheduling input given in isolation. Schedules do notcreate Participants collaborate to meet users needs meet their needs schedules that Our goal is to provide decision support tools that enable multiple participants to optimize schedules in a collaborative manner. Multi-Objective Scheduling- Issues • Effective scheduling of missions requires the ability to make trade-offs between competing objectives: – Time on target, minimizing use of consumables, minimizing the use of critical mechanisms, preferring the higher priority science • Objectives are often competing in that improving one objective means making another objective worse. • Objectives have different constituents lobbying for them – e.g. Mission science community versus Engineering • The traditional approach is to combine the weighted average of separate objectives – (Obj1 * wt1 + Obj2 * W2 …. + Objn * Wn) / n – Combining objectives loses information and pre-determines trade-offs between objectives. Multi-Objective - Solutions • Multi-Objective Scheduling: – Explicitly maintain and exploit multiple objectives during scheduling - Don’t combine objectives – Algorithms build up approximate Pareto optimal frontier • i.e. “non-dominated” solutions, such that no other candidate is better, considering all objectives. • Utilizing evolutionary algorithms (e.g. GDE3) QuickTime™ and a QuickTime™ and a PNG decompr essor PNG decompr essor ar e neede d to see this picture. ar e neede d to see this picture. Multi-Participant • The Pareto frontier gives participants an optimal trade-off space • Still need to agree on a particular candidate schedule • Multi-participant tools will provide distributed decision support – Mixed-initiative planning – support the end user in making trade offs • Automate when possible but leave final control with the user – Graphical internet-based tools that support multiple participants – Challenges include: human factors, non-simultaneous users, domain-specific scheduling GUIs Proposed model: threaded news/mail reader + schedule viewer JWST Scheduling Results - Pareto Optimal surfaces for each pair of objectives - Evaluated alternative search evolutionary approaches Cassini • Saturn orbiter + Titan lander – launched 1997 – arrived at Saturn 2004 • Science instruments include 6 for optical and microwave remote sensing, and 6 for fields/particles/waves investigations • Spectacular scientific success – 260 scientists from 17 countries participating – science objectives coordinated by 6 science discipline-oriented teams: Rings, Atmospheres, Titan, Icy Satellites, Magnetosphere, and Cross-Discipline (everything else) • ~1 Gigabyte per day science data returned • Prime mission completed; currently in first 2 year extension of prime mission: a second 2 year extension is expected Multi-Objective Cassini Science Planning: Example Future Work Q uic kT im e™ an d a T IFF ( Un co m pr e ss e d) d ec o mp r es s or Q uic kT im e™ an d a a re n ee d ed to s e e th is p ictu r e. T IFF ( Un co m pr e ss e d) d ec o mp r es s or a re n ee d ed to s e e th is p ictu r e. • Develop multi-participant capabilities – Threaded email model – DSN scheduling as an application • New capabilities in framework – Parallel evaluation of evolutionary algorithms • Apply framework to other applications – Planning HST phase 1 observations? – JWST long range planning?
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