Optimizing human activity patterns using global sensitivity analysis
Abstract
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. We generate schedules for a population that captures regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor), using the dynamic activity simulation engine, DASim. We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We demonstrate how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finnaly, though our tuning methods are applied to dynamic activity schedule generation, theymore »
- Authors:
-
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Defense Systems and Analysis Division
- Tulane Univ., New Orleans, LA (United States). Dept. of Mathematics
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). S.M. Mniszewski Computer, Computational, and Statistical Sciences Division
- Publication Date:
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1263499
- Alternate Identifier(s):
- OSTI ID: 1321728
- Report Number(s):
- LA-UR-13-22442
Journal ID: ISSN 1381-298X; PII: 9171
- Grant/Contract Number:
- AC52-06NA25396; GM097658-01
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computational and Mathematical Organization Theory
- Additional Journal Information:
- Journal Volume: 20; Journal Issue: 4; Journal ID: ISSN 1381-298X
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Global optimization; Global sensitivity analysis; Sample entropy; Agent-based modeling; Bayesian Gaussian process regression; Harmony search; 99 GENERAL AND MISCELLANEOUS
Citation Formats
Fairchild, Geoffrey, Hickmann, Kyle S., Mniszewski, Susan M., Del Valle, Sara Y., and Hyman, James M. Optimizing human activity patterns using global sensitivity analysis. United States: N. p., 2013.
Web. doi:10.1007/s10588-013-9171-0.
Fairchild, Geoffrey, Hickmann, Kyle S., Mniszewski, Susan M., Del Valle, Sara Y., & Hyman, James M. Optimizing human activity patterns using global sensitivity analysis. United States. https://doi.org/10.1007/s10588-013-9171-0
Fairchild, Geoffrey, Hickmann, Kyle S., Mniszewski, Susan M., Del Valle, Sara Y., and Hyman, James M. Tue .
"Optimizing human activity patterns using global sensitivity analysis". United States. https://doi.org/10.1007/s10588-013-9171-0. https://www.osti.gov/servlets/purl/1263499.
@article{osti_1263499,
title = {Optimizing human activity patterns using global sensitivity analysis},
author = {Fairchild, Geoffrey and Hickmann, Kyle S. and Mniszewski, Susan M. and Del Valle, Sara Y. and Hyman, James M.},
abstractNote = {Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. We generate schedules for a population that captures regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor), using the dynamic activity simulation engine, DASim. We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We demonstrate how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finnaly, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.},
doi = {10.1007/s10588-013-9171-0},
journal = {Computational and Mathematical Organization Theory},
number = 4,
volume = 20,
place = {United States},
year = {Tue Dec 10 00:00:00 EST 2013},
month = {Tue Dec 10 00:00:00 EST 2013}
}
Web of Science
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