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Planning and Scheduling Under Uncertainty

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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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Presentation Transcript
slide1
CMU PAL

Planning and SchedulingUnder Uncertainty

problem
Assignment of offices
  • Reservation of conference rooms
  • Allocation of office equipment
Problem

Automated management of resources,

in both routine and crisis situations.

challenges
Challenges
  • Intelligent management of available resources
  • Collaboration with users
  • Continuous learning of new knowledge and strategies
year 1 office allocation
Six-minute videoYear 1: Office allocation

A prototype system for automated

allocation of offices.

  • Effective allocation of office resources
  • Interface for a human administrator
years 2 3 conference planning
Six-minute demoYears 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
architecture
Parser

Optimizer

Info elicitor

Updateresourceallocation

Chooseand sendquestions

Graphicaluser interface

Administrator

Architecture

Top-level control

and learning

Processnew info

experiments with optimizer
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

experiments with elicitor
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.

main results
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
potential boeing applications
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