IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v54y2016i21p6494-6511.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2016.1205760
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207543.2016.1205760?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alexopoulos, Christos & Goldsman, David & Fontanesi, John & Kopald, David & Wilson, James R., 2008. "Modeling patient arrivals in community clinics," Omega, Elsevier, vol. 36(1), pages 33-43, February.
    2. Zhang, Hong & Tam, C. M. & Li, Heng, 2005. "Modeling uncertain activity duration by fuzzy number and discrete-event simulation," European Journal of Operational Research, Elsevier, vol. 164(3), pages 715-729, August.
    3. David Sinreich & Ola Jabali, 2007. "Staggered work shifts: a way to downsize and restructure an emergency department workforce yet maintain current operational performance," Health Care Management Science, Springer, vol. 10(3), pages 293-308, September.
    4. Mary A. Johnson & Sanghoon Lee & James R. Wilson, 1994. "Experimental Evaluation of a Procedure for Estimating Nonhomogeneous Poisson Processes Having Cyclic Behavior," INFORMS Journal on Computing, INFORMS, vol. 6(4), pages 356-368, November.
    5. Arslan, Olcay, 2009. "Maximum likelihood parameter estimation for the multivariate skew-slash distribution," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2158-2165, October.
    6. Kuhl, Michael E. & Wilson, James R., 2001. "Modeling and simulating Poisson processes having trends or nontrigonometric cyclic effects," European Journal of Operational Research, Elsevier, vol. 133(3), pages 566-582, September.
    7. Zied Jemai & L. Aboueljinane & E. Sahin, 2013. "A review on simulation models applied to emergency medical service operations," Post-Print hal-01672393, HAL.
    8. Lawrence M. Leemis, 1991. "Nonparametric Estimation of the Cumulative Intensity Function for a Nonhomogeneous Poisson Process," Management Science, INFORMS, vol. 37(7), pages 886-900, July.
    9. Izady, Navid & Worthington, Dave, 2012. "Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments," European Journal of Operational Research, Elsevier, vol. 219(3), pages 531-540.
    10. Ahmed, Mohamed A. & Alkhamis, Talal M., 2009. "Simulation optimization for an emergency department healthcare unit in Kuwait," European Journal of Operational Research, Elsevier, vol. 198(3), pages 936-942, November.
    11. Abo-Hamad, Waleed & Arisha, Amr, 2013. "Simulation-based framework to improve patient experience in an emergency department," European Journal of Operational Research, Elsevier, vol. 224(1), pages 154-166.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marco Boresta & Tommaso Giovannelli & Massimo Roma, 2024. "Managing low–acuity patients in an Emergency Department through simulation–based multiobjective optimization using a neural network metamodel," Health Care Management Science, Springer, vol. 27(3), pages 415-435, September.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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".

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yong-Hong Kuo & Omar Rado & Benedetta Lupia & Janny M. Y. Leung & Colin A. Graham, 2016. "Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 120-147, June.
    2. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    3. Michael E. Kuhl & Sachin G. Sumant & James R. Wilson, 2006. "An Automated Multiresolution Procedure for Modeling Complex Arrival Processes," INFORMS Journal on Computing, INFORMS, vol. 18(1), pages 3-18, February.
    4. Na Li & Xiaorui Li & Paul Forero, 2022. "Physician scheduling for outpatient department with nonhomogeneous patient arrival and priority queue," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 879-915, December.
    5. Ran Liu & Xiaolan Xie, 2018. "Physician Staffing for Emergency Departments with Time-Varying Demand," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 588-607, August.
    6. Miguel Angel Ortíz-Barrios & Juan-José Alfaro-Saíz, 2020. "Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-41, April.
    7. Mark Bebbington & Ričardas Zitikis, 2004. "A Robust Heuristic Estimator for the Period of a Poisson Intensity Function," Methodology and Computing in Applied Probability, Springer, vol. 6(4), pages 441-462, December.
    8. 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".
    9. Omar EL-Rifai & Thierry Garaix & Vincent Augusto & Xiaolan Xie, 2015. "A stochastic optimization model for shift scheduling in emergency departments," Health Care Management Science, Springer, vol. 18(3), pages 289-302, September.
    10. 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.
    11. Glasgow, Simon M. & Perkins, Zane B. & Tai, Nigel R.M. & Brohi, Karim & Vasilakis, Christos, 2018. "Development of a discrete event simulation model for evaluating strategies of red blood cell provision following mass casualty events," European Journal of Operational Research, Elsevier, vol. 270(1), pages 362-374.
    12. Kaushal, Arjun & Zhao, Yuancheng & Peng, Qingjin & Strome, Trevor & Weldon, Erin & Zhang, Michael & Chochinov, Alecs, 2015. "Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 18-31.
    13. Huifen Chen & Bruce Schmeiser, 2013. "I-SMOOTH: Iteratively Smoothing Mean-Constrained and Nonnegative Piecewise-Constant Functions," INFORMS Journal on Computing, INFORMS, vol. 25(3), pages 432-445, August.
    14. Abo-Hamad, Waleed & Arisha, Amr, 2013. "Simulation-based framework to improve patient experience in an emergency department," European Journal of Operational Research, Elsevier, vol. 224(1), pages 154-166.
    15. Yiting Xing & Ling Li & Zhuming Bi & Marzena Wilamowska‐Korsak & Li Zhang, 2013. "Operations Research (OR) in Service Industries: A Comprehensive Review," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 300-353, May.
    16. Duma, Davide & Aringhieri, Roberto, 2023. "Real-time resource allocation in the emergency department: A case study," Omega, Elsevier, vol. 117(C).
    17. Alexopoulos, Christos & Goldsman, David & Fontanesi, John & Kopald, David & Wilson, James R., 2008. "Modeling patient arrivals in community clinics," Omega, Elsevier, vol. 36(1), pages 33-43, February.
    18. Ellen Bockstal & Broos Maenhout, 2019. "A study on the impact of prioritising emergency department arrivals on the patient waiting time," Health Care Management Science, Springer, vol. 22(4), pages 589-614, December.
    19. James T. Lin & Chun-Chih Chiu, 2018. "A hybrid particle swarm optimization with local search for stochastic resource allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 481-495, March.
    20. Xi Chen & Dave Worthington, 2017. "Staffing of time-varying queues using a geometric discrete time modelling approach," Annals of Operations Research, Springer, vol. 252(1), pages 63-84, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:54:y:2016:i:21:p:6494-6511. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.