Optimizing human activity patterns using global sensitivity analysis

• Published in: Computational and mathematical organization theory 2014-12, Vol.20 (4), p.394-416
• Main Authors: Fairchild, Geoffrey, Hickmann, Kyle S., Mniszewski, Susan M., Del Valle, Sara Y., Hyman, James M.
• Format: Article
• Language: English
• Subjects: Agent-based modeling; Algorithms; Artificial Intelligence; Bayesian analysis; Bayesian Gaussian process regression; Bayesian method; Business and Management; Computation; Computational methods; Computer simulation; Computers; Epidemics; GENERAL AND MISCELLANEOUS; Global optimization; Global sensitivity analysis; Harmony search; Laboratories; Management; Mathematical models; MATHEMATICS AND COMPUTING; Methodology of the Social Sciences; Normal distribution; Operations Research/Decision Theory; Optimization; Organization theory; Population; Realism; Sample entropy; Schedules; Searching; Sensitivity analysis; Shopping; Simulation; Sociology; Statistical analysis; Studies; Supply & demand; Tuning; Variance analysis
• ISSN: 1381-298X; 1572-9346
• DOI: 10.1007/s10588-013-9171-0

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.