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