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Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines

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  • Thomas Monks
  • Christine S. M. Currie
  • Bhakti Stephan Onggo
  • Stewart Robinson
  • Martin Kunc
  • Simon J. E. Taylor

Abstract

This study develops a standardised checklist approach to improve the reporting of discrete-event simulation, system dynamics and agent-based simulation models within the field of Operational Research and Management Science. Incomplete or ambiguous reporting means that many simulation studies are not reproducible, leaving other modellers with an incomplete picture of what has been done and unable to judge the reliability of the results. Crucially, unclear reporting makes it difficult to reproduce or reuse findings. In this paper, we review the evidence on the quality of model reporting and consolidate previous work. We derive general good practice principles and three 20-item checklists aimed at Strengthening The Reporting of Empirical Simulation Studies (STRESS): STRESS-DES, STRESS-ABS and STRESS-SD for discrete-event simulation, agent-based simulation and system dynamics, respectively. Given the variety of simulation projects, we provide usage and troubleshooting advice to cover a wide range of situations.

Suggested Citation

  • Thomas Monks & Christine S. M. Currie & Bhakti Stephan Onggo & Stewart Robinson & Martin Kunc & Simon J. E. Taylor, 2019. "Strengthening the reporting of empirical simulation studies: Introducing the STRESS guidelines," Journal of Simulation, Taylor & Francis Journals, vol. 13(1), pages 55-67, January.
    • Handle: RePEc:taf:tjsmxx:v:13:y:2019:i:1:p:55-67
    DOI: 10.1080/17477778.2018.1442155
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    Cited by:

    1. Monks, Thomas & Harper, Alison & Anagnostou, Anastasia & Taylor, Simon J.E., 2022. "Open Science for Computer Simulation," OSF Preprints zpxtm, Center for Open Science.
    2. Maren S. Barth & Katharina Palm & Henrik Andersson & Tobias A. Granberg & Anders N. Gullhav & Andreas Krüger, 2024. "Emergency exit layout planning using optimization and agent-based simulation," Computational Management Science, Springer, vol. 21(1), pages 1-25, June.
    3. Nieqing Cao & Austin Marcus & Lubna Altarawneh & Soongeol Kwon, 2023. "Priority-based replenishment policy for robotic dispensing in central fill pharmacy systems: a simulation-based study," Health Care Management Science, Springer, vol. 26(2), pages 344-362, June.
    4. Nguyen, Le Khanh Ngan & Howick, Susan & Megiddo, Itamar, 2024. "A framework for conceptualising hybrid system dynamics and agent-based simulation models," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1153-1166.

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