title: 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. 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. 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. 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.
authors.affiliation: Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
authors.forename: Geoffrey
authors.initials: G
authors.lastname: Fairchild
authors.affiliation: Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA.
authors.forename: Kyle S
authors.initials: KS
authors.lastname: Hickmann
authors.affiliation: Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
authors.forename: Susan M
authors.initials: SM
authors.lastname: Mniszewski
authors.affiliation: Defense Systems and Analysis Division, Los Alamos National Laboratory, Los Alamos, NM, USA.
authors.forename: Sara Y
authors.initials: SY
authors.lastname: Del Valle
authors.affiliation: Department of Mathematics, Center for Computational Science, Tulane University, New Orleans, LA, USA.
authors.forename: James M
authors.initials: JM
authors.lastname: Hyman
keywords: Agent-based modeling; Bayesian Gaussian process regression; Global optimization; Global sensitivity analysis; Harmony search; Sample entropy