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Planning and Scheduling Under Uncertainty

CMU PAL. Planning and Scheduling Under Uncertainty. Assignment of offices Reservation of conference rooms Allocation of office equipment. Problem. Automated management of resources, in both routine and crisis situations. Challenges. Intelligent management of available resources.

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Planning and Scheduling Under Uncertainty

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  1. CMU PAL Planning and SchedulingUnder Uncertainty

  2. Assignment of offices • Reservation of conference rooms • Allocation of office equipment Problem Automated management of resources, in both routine and crisis situations.

  3. Challenges • Intelligent management of available resources • Collaboration with users • Continuous learning of new knowledge and strategies

  4. Six-minute video Year 1: Office allocation A prototype system for automated allocation of offices. • Effective allocation of office resources • Interface for a human administrator

  5. Six-minute demo Years 2–3: Conference planning Scheduling of talks at a conference, and related allocation of rooms and equipment, in a crisis situation. • Uncertainty tolerance • Information elicitation • Collaboration with ahuman administrator

  6. Parser Optimizer Info elicitor Updateresourceallocation Chooseand sendquestions Graphicaluser interface Administrator Architecture Top-level control and learning Processnew info

  7. Manual and auto scheduling Search time ScheduleQuality ScheduleQuality 0.83 0.83 0.80 0.78 0.72 Auto Auto Auto 0.63 Manual 0.9 Manual Manual 0.8 0.7 0.6 4 1 3 9 2 5 6 7 8 10 13 rooms 84 events 5 rooms 32 events 9 rooms 62 events Time (seconds) 13 rooms 84 events problem size Experiments with Optimizer without uncertainty with uncertainty

  8. Dependency of the qualityon the number of questions Manual and auto repair ScheduleQuality ScheduleQuality 0.72 0.68 0.72 0.61 Auto withElicitation 0.50 Auto w/oElicitation ManualRepair After Crisis 0.68 10 30 40 50 20 Number of Questions Experiments with Elicitor We have applied the system to repair a schedule after a “crisis” loss of rooms.

  9. Main results • Resource management in both crisis and routine situations • Optimization under uncertainty • Collaboration with the user • Elicitation of additional information • Learning of typical requirementsand user preferences • Contingency scheduling

  10. Potential Boeing applications • Optimization: Improvement of resource utilization and field-service efficiency • Elicitation: Identification of important missing information • Learning: Adaptation to changing users, operational conditions, and resources • Contingency reasoning: Construction of backup plans for possible scenarios

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