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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

    as
    1. Marrel, Amandine & Iooss, Bertrand & Laurent, Béatrice & Roustant, Olivier, 2009. "Calculations of Sobol indices for the Gaussian process metamodel," Reliability Engineering and System Safety, Elsevier, vol. 94(3), pages 742-751.
    2. S. M. Mniszewski & S. Y. Del Valle & P. D. Stroud & J. M. Riese & S. J. Sydoriak, 2008. "Pandemic simulation of antivirals + school closures: buying time until strain-specific vaccine is available," Computational and Mathematical Organization Theory, Springer, vol. 14(3), pages 209-221, September.
    3. Bhat, Chandra R. & Frusti, Teresa & Zhao, Huimin & Schönfelder, Stefan & Axhausen, Kay W., 2004. "Intershopping duration: an analysis using multiweek data," Transportation Research Part B: Methodological, Elsevier, vol. 38(1), pages 39-60, January.
    4. Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
    5. Fred Glover, 1990. "Tabu Search—Part II," INFORMS Journal on Computing, INFORMS, vol. 2(1), pages 4-32, February.
    6. Kitamura, Ryuichi & Yamamoto, Toshiyuki & Susilo, Yusak O. & Axhausen, Kay W., 2006. "How routine is a routine? An analysis of the day-to-day variability in prism vertex location," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 259-279, March.
    7. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    8. Phillip Stroud & Sara Del Valle & Stephen Sydoriak & Jane Riese & Susan Mniszewski, 2007. "Spatial Dynamics of Pandemic Influenza in a Massive Artificial Society," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-9.
    9. Ryuichi Kitamura & Cynthia Chen & Ram Pendyala & Ravi Narayanan, 2000. "Micro-simulation of daily activity-travel patterns for travel demand forecasting," Transportation, Springer, vol. 27(1), pages 25-51, February.
    10. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    11. Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769, August.
    12. Richard M. Burton, 2003. "Computational Laboratories for Organization Science: Questions, Validity and Docking," Computational and Mathematical Organization Theory, Springer, vol. 9(2), pages 91-108, July.
    13. D. Brockmann & L. Hufnagel & T. Geisel, 2006. "The scaling laws of human travel," Nature, Nature, vol. 439(7075), pages 462-465, January.
    14. Jeremy Oakley, 2002. "Bayesian inference for the uncertainty distribution of computer model outputs," Biometrika, Biometrika Trust, vol. 89(4), pages 769-784, December.
    15. Vittoria Colizza & Alain Barrat & Marc Barthelemy & Alain-Jacques Valleron & Alessandro Vespignani, 2007. "Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions," PLOS Medicine, Public Library of Science, vol. 4(1), pages 1-16, January.
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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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