Optimizing human activity patterns using global sensitivity analysis

Journal Article · Tue Dec 10 00:00:00 UTC 2013 · Computational and Mathematical Organization Theory

DOI: https://doi.org/10.1007/s10588-013-9171-0 · OSTI ID:1263499

Fairchild, Geoffrey ^[1]; Hickmann, Kyle S. ^[2]; Mniszewski, Susan M. ^[3]; Del Valle, Sara Y. ^[1]; Hyman, James M. ^[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

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, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-06NA25396
• OSTI ID: 1263499
• Report Number(s): LA-UR--13-22442; PII: 9171
• Journal Information: Computational and Mathematical Organization Theory, Journal Name: Computational and Mathematical Organization Theory Journal Issue: 4 Vol. 20; ISSN 1381-298X
• Country of Publication: United States
• Language: English