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Title: Optimizing human activity patterns using global sensitivity analysis

Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show 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. Here 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. Finally, though our tuning methods are applied to dynamic activity schedule generation,more » they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.« less
 [1] ;  [2] ;  [3] ;  [1] ;  [2]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Defense Systems and Analysis Division
  2. Tulane Univ., New Orleans, LA (United States). Dept. of Mathematics
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). S.M. Mniszewski Computer, Computational, and Statistical Sciences Division
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1381-298X; PII: 9171
Grant/Contract Number:
AC52-06NA25396; GM097658-01
Accepted Manuscript
Journal Name:
Computational and Mathematical Organization Theory
Additional Journal Information:
Journal Volume: 20; Journal Issue: 4; Journal ID: ISSN 1381-298X
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; Global optimization; Global sensitivity analysis; Sample entropy; Agent-based modeling; Bayesian Gaussian process regression; Harmony search; 99 GENERAL AND MISCELLANEOUS