Evaluation and selection of medical tourism sites: A rough analytic hierarchy process based multi‐attributive border approximation area comparison approach

Abstract

This paper presents a novel Multiple Criteria Decision Making methodology for assessing and prioritizing medical tourism destinations under uncertainty. A systematic evaluation and assessment approach is proposed by incorporating analytic hierarchy process and multi‐attributive border approximation area comparison methods in the rough environment. Rough number is used to aggregate individual judgements of decision makers and express their true perception to handle vagueness without any prior information. Rough analytic hierarchy process analyses the relative importance of criteria based on their preferences given by experts, whereas rough multi‐attributive border approximation area comparison evaluates the alternative sites based on the criteria weights. A case study of prioritizing different sites (cities) in India for medical tourism services is shown to demonstrate the applicability of the proposed method. Among different criteria “quality of infrastructure of healthcare institutions” is observed to be the most important criteria in our analysis, followed by “supply of skilled human resources and new job creations” and “Chennai” is found to be the best medical tourism site in India. Finally, a comparative analysis and validity testing of the proposed method are elaborated, and the methodology provides a standard for select medical tourism sites on the basis of different criteria.

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

“…FS, IFS, and SVNS have attracted many researchers because they are more simple and more effective than the rough set (Roy, Chatterjee, Bandyopadhyay, & Kar, ) and other extensions of FS, such as fuzzy soft set (Das, Ghosh, Kar, & Pal, ), neutrosophic soft set (Das, Kumar, Kar, & Pal, ), hesitant fuzzy soft set (Das, Malakar, Kar, & Pal, ), hesitant FS (Hu, Yang, Zhang, & Chen, ; Sun, Hu, Zhou, & Chen, ), and picture hesitant FS (Yang, Hu, Liu, & Chen, ).…”

“…In addition, some studies have applied SVNS to multiple criteria decision making (M. Radwan, Senousy, & M. Riad, 2016;Yang, Hu, Sun, & Chen, 2018;Ye, 2017). FS, IFS, and SVNS have attracted many researchers because they are more simple and more effective than the rough set (Roy, Chatterjee, Bandyopadhyay, & Kar, 2018) and other extensions of FS, such as fuzzy soft set (Das, Ghosh, Kar, & Pal, 2017), neutrosophic soft set (Das, Kumar, Kar, & Pal, 2017), hesitant fuzzy soft set (Das, Malakar, Kar, & Pal, 2017), hesitant FS Sun, Hu, Zhou, & Chen, 2018), and picture hesitant FS ).…”

Multi Criteria Evaluation Framework for Prioritizing Indian Railway Stations Using Modified Rough AHP-Mabac Method

“…(1) to (6). The detailed calculation procedure can be found in Roy et al (2018). The corresponding rough number is .…”

“…The fuzzy set theory has been studied in various dimensions to handle actual life inaccurate hitches (Roy et al, 2016a(Roy et al, , 2016bChen, 2000). Fuzzy methodology tackles the inaccuracy very efficiently, but it requires very previous data or strong association element to result in an effective decision at a particular interval of described time (Qazi et al, 2016;Wang et al, 2016;Roy et al, 2018). Rough set theory use to alter the roughness of a data, which has been successfully applied to various real life decision making problems (Pawlak, 1991;Pawlak, 1982;Zheng et al, 2016;Zhai et al, 2010;Khoo and Zhai, 2001;Liang et al, 2016).…”

“…Most of the research works related to railways development and management are based on crisp data or limited to fuzzy approaches which need much prior data. Many authors (Roy et al 2018;Zhai et al, 2008Zhai et al, , 2009) have asserted the superiority of rough numbers over fuzzy approaches in manipulating subjective and vague information. To the best of our knowledge, till date, we have not found any article or research paper which investigated railways problems using such a technique deals uncertainty.…”