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Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis

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  • Muhammet Gul
  • Erkan Celik
  • Alev Taskin Gumus
  • Ali Fuat Guneri

Abstract

Emergency departments (EDs) have an important position in healthcare system environment because of their serviceability to the patients with several illnesses and injuries at all hours of the day and night. Therefore, efficient management of these departments by executives and service providers is only possible with effective tools such as simulation. The concept of multiple criteria decision-making (MCDM) is applicable by computer simulation for decision analysis and performance evaluation of an ED. This study proposes an integration of computer simulation, MCDM methods of interval type-2 fuzzy analytic hierarchy process (IT2FAHP), and ELECTRE for an ED system in a university hospital. Due to the high uncertainty in ED operations, interval type-2 fuzzy sets (IT2FSs) are used in MCDM process. Consequently, the integrated method can be used as a useful tool to evaluate the ED performance and select the best scenario considering different numbers of nurses and doctors for three shifts. [Received 30 July 2013; Revised 9 February 2015; Accepted 14 June 2015]

Suggested Citation

  • Muhammet Gul & Erkan Celik & Alev Taskin Gumus & Ali Fuat Guneri, 2016. "Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(2), pages 196-223.
  • Handle: RePEc:ids:eujine:v:10:y:2016:i:2:p:196-223
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    References listed on IDEAS

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    1. Umut Rıfat Tuzkaya & Sunderesh S. Heragu & Gerald W. Evans & Michael Johnson, 2014. "Designing a large-scale emergency logistics network - a case study for Kentucky," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 8(4), pages 513-532.
    2. Hatami-Marbini, Adel & Tavana, Madjid, 2011. "An extension of the Electre I method for group decision-making under a fuzzy environment," Omega, Elsevier, vol. 39(4), pages 373-386, August.
    3. Kuo, Yiyo & Yang, Taho & Cho, Chiwoon & Tseng, Yao-Ching, 2008. "Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(1), pages 40-56.
    4. 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.
    5. Celik, Erkan & Bilisik, Ozge Nalan & Erdogan, Melike & Gumus, Alev Taskin & Baracli, Hayri, 2013. "An integrated novel interval type-2 fuzzy MCDM method to improve customer satisfaction in public transportation for Istanbul," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 58(C), pages 28-51.
    6. Celik, Erkan & Aydin, Nezir & Gumus, Alev Taskin, 2014. "A multiattribute customer satisfaction evaluation approach for rail transit network: A real case study for Istanbul, Turkey," Transport Policy, Elsevier, vol. 36(C), pages 283-293.
    7. Wang, Xiaoting & Triantaphyllou, Evangelos, 2008. "Ranking irregularities when evaluating alternatives by using some ELECTRE methods," Omega, Elsevier, vol. 36(1), pages 45-63, February.
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    Cited by:

    1. Mimica R. Milošević & Dušan M. Milošević & Dragan M. Stević & Ana D. Stanojević, 2019. "Smart City: Modeling Key Indicators in Serbia Using IT2FS," Sustainability, MDPI, vol. 11(13), pages 1-28, June.
    2. Sun-Weng Huang & James J. H. Liou & Hai-Hua Chuang & Gwo-Hshiung Tzeng, 2021. "Using a Modified VIKOR Technique for Evaluating and Improving the National Healthcare System Quality," Mathematics, MDPI, vol. 9(12), pages 1-21, June.

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