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Optimizing human activity patterns using global sensitivity analysis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Geoffrey Fairchild & Kyle S. Hickmann & Susan M. Mniszewski & Sara Y. Del Valle & James M. Hyman, 2014. "Optimizing human activity patterns using global sensitivity analysis," Computational and Mathematical Organization Theory, Springer, vol. 20(4), pages 394-416, December.
    • Handle: RePEc:spr:comaot:v:20:y:2014:i:4:d:10.1007_s10588-013-9171-0
    DOI: 10.1007/s10588-013-9171-0
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    References listed on IDEAS

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    2. Behrouz Pirouz & Sina Shaffiee Haghshenas & Sami Shaffiee Haghshenas & Patrizia Piro, 2020. "Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligenc," Sustainability, MDPI, vol. 12(6), pages 1-21, March.
    3. Jin Hee Yoon & Zong Woo Geem, 2021. "Empirical Convergence Theory of Harmony Search Algorithm for Box-Constrained Discrete Optimization of Convex Function," Mathematics, MDPI, vol. 9(5), pages 1-13, March.

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