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Using simulation and optimisation to characterise durations of emergency department service times with incomplete data

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  • Hainan Guo
  • David Goldsman
  • Kwok-Leung Tsui
  • Yu Zhou
  • Shui-Yee Wong

Abstract

Simulation models of emergency departments (EDs) are often built based on incomplete data, for example, missing arrival times or service-time durations. The difficulty in collecting reliable and complete data can subsequently lead to invalid simulation results. To tackle this problem, we propose a simulation and optimisation method to characterise the unavailable durations of service times. Since many services in an ED are sequential and dependent on each other, this paper considers these multiple process steps cooperatively. We first use lognormal distributions to characterise the key service durations. Then we propose a new meta-heuristic approach, which combines an Improved Adaptive Genetic Algorithm (AGA) and Simulated Annealing (SA), IAGASA, to search for the optimal set of service-time distribution parameters. To address the difficulties of applying IAGASA when noise is involved in the performance measures and improve the simulation efficiency, we jointly apply IAGASA and Optimal Computing Budget Allocation (OCBA) technology. OCBA minimises the total simulation cost for achieving a desired level of probability of correctly selecting the best set of distribution parameters, which improves the search efficiency significantly. The experimental results indicate that our proposed method can find accurate estimates of service-time distribution parameters within a relatively short time.

Suggested Citation

  • Hainan Guo & David Goldsman & Kwok-Leung Tsui & Yu Zhou & Shui-Yee Wong, 2016. "Using simulation and optimisation to characterise durations of emergency department service times with incomplete data," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6494-6511, November.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:21:p:6494-6511
    DOI: 10.1080/00207543.2016.1205760
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    Cited by:

    1. Alberto Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2023. "A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department," Annals of Operations Research, Springer, vol. 320(2), pages 727-756, January.
    2. Alberto De Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2022. "Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of Emergency Department patient arrivals," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 979-1012, December.
    3. Hainan Guo & Haobin Gu & Yu Zhou & Jiaxuan Peng, 2022. "A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 238-262, June.
    4. Hailiang Wang & Jiaxin Zhang & Yan Luximon & Mingfu Qin & Ping Geng & Da Tao, 2022. "The Determinants of User Acceptance of Mobile Medical Platforms: An Investigation Integrating the TPB, TAM, and Patient-Centered Factors," IJERPH, MDPI, vol. 19(17), pages 1-17, August.
    5. Alberto De Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2020. "An optimal non-uniform piecewise constant approximation for the patient arrival rate for a more efficient representation of the Emergency Departments arrival process," DIAG Technical Reports 2020-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

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