Space Allocation Optimization
at NASA Langley Research Center
Rex K. Kincaid, College of William & Mary
Robert Gage, NASA Langley Research Center
Raymond 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
Center Characteristics
•
•
•
•
3,500 employees
6,200 rooms
1,600 labs
300 buildings
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
System Architecture
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
• Existing Data
 Personnel
 Space Utilization
 GIS Center and Floor Plan Spatial Data
• New Data
 Technical Space Features
 Technical Function Requirements
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
• 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
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
Low Level Algorithmic Components
Data Analysis / Preparation
• Filter and Classify Input Data
• Problem Domain Reduction
• Examples
 Classify Personnel for Space Requirements
 Determine Pools of Compatible Space
Data Sources
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
Low Level Algorithmic Components
• 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
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
• 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
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
• Examples
 Constraints




Space Compatibility
Minimal Area Requirements
Consumer Compatibility
Metrics



Move Cost
Office Area Per Person
Synergy
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
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
User Interface
High Level Algorithmic Components
• Components for modeling techniques for
searching problem domain
• Examples
 Local Greedy Heuristic
 Random Search, Tabu Search,
Simulated Annealing, Genetic
Algorithms, Hybrid Techniques
Mid Level Algorithmic Components
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
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