Space Allocation Optimization at NASA Langley Research Center Rex K. Kincaid, College of William &...
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Transcript of Space Allocation Optimization at NASA Langley Research Center Rex K. Kincaid, College of William &...
Space Allocation Optimizationat NASA Langley Research Center
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Rex K. Kincaid, College of William & MaryRobert Gage, NASA Langley Research CenterRaymond Gates, NASA Langley Research Center
Goals
• Integrated planning system– Schedule allocation of office and technical space
based on current and projected organizational and project requirements
– Maintain organizational synergy by co-locating within/between related organizations
– Comply with space guidelines/requirements– Plan for changes in available space due to new
construction, demolition, rehab, lease– Minimize moves– Save money
• 3,500 employees • 6,200 rooms• 1,600 labs• 300 buildings
Center Characteristics
Visualization
• Problems– Buildings are
sparsely distributed– Disjoint E/W areas– Floors overlay– Difficult to provide a
single image that conveys all the details necessary
Visualization
• Spatial Subdivision Diagram
– Permits display of large amounts of information in a compact form
– Rectangular features are proxies for the actual spatial entities such as buildings
– Features are scaled relatively to represent any quantity such as gross area, office area, or capacity
Data Sources
Data Analysis / Preparation
Low Level Algorithmic Components
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
System Architecture
Data Sources
Data Analysis / Preparation
Low Level Algorithmic Components
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
• Existing Data Personnel Space Utilization GIS Center and Floor Plan Spatial Data
• New Data Technical Space Features Technical Function Requirements
Data Sources
Data Analysis / Preparation
Low Level Algorithmic Components
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
• Dynamic Inconsistent and continually changing Planned and unplanned changes Planning based on snapshots Need to be reconciled often
Monthly Move Data Histogram
Monthly Move Data Histograms
Details of Move Data
Time Period A: 8 months (July 2004—February 2005)
- 1,791 total moves
- 335 moves within same building
Time Period B: 22 months (March 2005—December 2006)
- 455 total moves
- 7% of employees move each year
- 13 moves within same building
Data Sources
Data Analysis / Preparation
Low Level Algorithmic Components
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
• Filter and Classify Input Data• Problem Domain Reduction• Examples
Classify Personnel for Space Requirements Determine Pools of Compatible Space
Data Sources
Low Level Algorithmic Components
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
•Components for modeling aspects of optimization problem
•Examples Space represents areas to be
assigned, i.e. rooms Consumers represent any function
that consumes space, i.e. people, technical functions, conference areas
Data Analysis / Preparation
Data Sources
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
• Components for modeling requirements and goals of optimization problem
Constraints Minimum necessary conditions May reduce problem domain
Metrics Define the measures for an optimal
solution Use a cost-based minimization
approach
Data Analysis / Preparation
Low Level Algorithmic Components
Data Sources
Mid Level Algorithmic Components
High Level Algorithmic Components
User Interface
• Examples Constraints
Space Compatibility Minimal Area Requirements Consumer Compatibility
Metrics Move Cost Office Area Per Person Synergy
Data Analysis / Preparation
Low Level Algorithmic Components
System Architecture
• Synergy Metric– Hierarchical, flat interaction model assumes
equal interaction between peers in each organization
– Reality is different– Organizations self-organize– Use current allocation to find probable
interactions
Data Sources
High Level Algorithmic Components
User Interface
• Components for modeling techniques for searching problem domain
• Examples Local Greedy Heuristic Random Search, Tabu Search,
Simulated Annealing, Genetic Algorithms, Hybrid Techniques
Data Analysis / Preparation
Low Level Algorithmic Components
Mid Level Algorithmic Components
Search Techniques
• Large Search Space– Exhaustive Search
not possible– Find the best local
optima in a limited amount of time
Search Techniques
• Greedy Approach– From a random starting
point, proceed in the most downhill direction
– compare features of local optima
• Beyond Greedy – implement simple tabu
search
Current NASA configuration
Local Optimum: NASA Space Allocation
Status
• Visualization tools largely complete• Primary metrics and constraints for
personnel defined and implemented• Greedy Heuristic implemented to search
from any initial state to a local optimum• Continuing to tune heuristic to improve
speed and adjust definition of local neighborhood with new operators
Status
• Plan to extend local search by including simple tabu search features
• Plan to experiment with long term memory by keeping track of high (low) quality partial solutions