Optimizing human activity patterns using global sensitivity analysis

Geoffrey Fairchild (), Kyle S. Hickmann, Susan M. Mniszewski, Sara Y. Del Valle and James M. Hyman
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Geoffrey Fairchild: Los Alamos National Laboratory
Kyle S. Hickmann: Tulane University
Susan M. Mniszewski: Los Alamos National Laboratory
Sara Y. Del Valle: Los Alamos National Laboratory
James M. Hyman: Tulane University

Computational and Mathematical Organization Theory, 2014, vol. 20, issue 4, No 3, 394-416

Abstract: 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.

Keywords: Global optimization; Global sensitivity analysis; Sample entropy; Agent-based modeling; Bayesian Gaussian process regression; Harmony search (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10588-013-9171-0

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