Medical device selection in private hospitals by integrated fuzzy MCGDM methods: a case study in choosing MRI (Magnetic Resonance Imaging) system

Kundu, Pradip; Görçün, Ömer Faruk; Küçükönder, Hande

doi:10.1080/01605682.2021.1960910

Cited by 14 publications

(2 citation statements)

References 37 publications

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“…Gangwar et al [ 63 ] utilized F–PSI to select the most suitable alternative ship hull material based on the properties of aluminium metal alloy. Kundu et al [ 64 ] employed F–PSI to select magnetic resonance imaging devices for private hospitals. Toslak et al [ 65 ] utilized F–PSI, SV, and MARCOS methods for selecting peanut butter machines.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment

Demir,

Moslem

2024

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment

Demir,

Moslem

2024

Heliyon

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“…Görçün et al ( 2023) evaluated logistics service providers for medical waste disposal treatment in the healthcare industry with the help of a novel integrated approach involving the extended form of the Delphi -SWARA -COPRAS approaches based on Interval Valued Fermatean fuzzy sets. Kundu et al (2021) assessed medical device selection in private hospitals using fuzzy MCGDM methods consisting of the fuzzy PSI and MARCOS combination. Zavadskas et al (2012) combined the WSM and WPM to create a unique utility degree-based MCDM method called WASPAS.…”

Section: Work On Healthcare Waste Managementmentioning

confidence: 99%

A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection

Rao

Sujatha

2023

Decis. Mak. Appl. Manag. Eng.

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Healthcare waste (HCW) management is a complex issue influenced by many factors, including technological, economic, environmental, and social factors. It is possible to regard the evaluation of the best treatment technique for HCW management as a challenging case of MCDM (multi-criteria decision-making), where various alternatives and evaluation criteria must be considered. The presentation and handling of the shaky data are crucial to choosing the HCW treatment technology. In order to address the issue of MCDM issues with Fermatean fuzzy (FF) data, we first build a consensus-based WASPAS approach in this study. In the suggested integrated methodology, the rank of the alternatives is determined using the WASPAS method in an FF environment, and the attribute weights are estimated using the entropy measure technique. In the preceding, an HCW treatment technology assessment issue is considered to make the proposed structure's applicability more transparent. In this study, four HCW treatment methods—chemical disinfection, microwave disinfection, cremation, and autoclaving—are considered options. According to the study's findings, autoclaving is the most effective HCW treatment method. Additionally, we demonstrate a sensitivity assessment using several criteria weight sets to test the stability of our intriguing proposed approach. We also call attention to a contrast between our suggested approach to decision-making and the practices now in use.

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An Overview of the Applications of BWM in Health

Tas

2023

Lecture Notes in Operations Research

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Medical device selection in private hospitals by integrated fuzzy MCGDM methods: a case study in choosing MRI (Magnetic Resonance Imaging) system

Cited by 14 publications

References 37 publications

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment

Evaluating the effect of the COVID-19 pandemic on medical waste disposal using preference selection index with CRADIS in a fuzzy environment

A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection

An Overview of the Applications of BWM in Health

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