Space Allocation Optimization at NASA Langley Research Center

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Space Allocation Optimizationat NASA Langley
Research Center
Rex K. Kincaid, College of William MaryRobert
Gage, NASA Langley Research CenterRaymond Gates,
NASA Langley Research Center

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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

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Center Characteristics

• 3,500 employees
• 6,200 rooms
• 1,600 labs
• 300 buildings

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Visualization

• Problems
• Buildings are sparsely distributed
• Disjoint E/W areas
• Floors overlay
• Difficult to provide a single image that conveys
  all the details necessary

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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

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System Architecture
User Interface
High Level Algorithmic Components
Mid Level Algorithmic Components
Low Level Algorithmic Components
Data Analysis / Preparation
Data Sources
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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

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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

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Monthly Move Data Histogram
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Monthly Move Data Histograms
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Details of Move Data

• Time Period A 8 months (July 2004February 2005)
• - 1,791 total moves
• - 335 moves within same building
• Time Period B 22 months (March 2005December
  2006)
• - 455 total moves
• - 7 of employees move each year
• - 13 moves within same building

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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
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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
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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
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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
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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

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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
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Search Techniques

• Large Search Space
• Exhaustive Search not possible
• Find the best local optima in a limited amount of
  time

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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

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Current NASA configuration
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Local Optimum NASA Space Allocation
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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

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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