Hospital Site Selection via Hesitant Fuzzy TOPSIS

Şenvar, Özlem; Otay, İrem; Boltürk, Eda

doi:10.1016/j.ifacol.2016.07.656

Cited by 83 publications

(39 citation statements)

References 19 publications

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“…This study used the fuzzy d proposed by [73], where the fuzzy positive and negative solutions were taken as ,0) respectively. years, many researchers have used the Fuzzy TOPSIS method for location selection determination of the best location for a wide range of places has been attempted, such plants [74], logistics centers [75][76][77], distribution centers [78,79], warehouses [80,81], ], hospitals [85], landfills [86], stations [87] and faculties [88]. The research shows that is a useful method to select the appropriate location for different kinds of facilities and the Problem of Location Selection by the Fuzzy TOPSIS Method pping malls have an increasing tendency to spread around the country, the issue of location remains prominent.…”

Section: Problem Of Location Selection By the Fuzzy Topsis Methodsmentioning

confidence: 99%

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

Erdin

Akbaş

2019

Sustainability

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Multi-criteria decision-making (MCDM) techniques are increasingly being used for the problem of location selection, which directly affects the long-term success of a company. Besides these techniques, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) also represent a useful method for selecting the appropriate location for different kinds of facilities and sites. In this respect, this study aims to compare the results of a MCDM technique, fuzzy technique for order preference by similarity to ideal solution (TOPSIS), and GIS for the location selection of shopping malls in Turkey. According to the results of both fuzzy TOPSIS and GIS, the Marmara region was determined as the best alternative for shopping malls in Turkey.Sustainability 2019, 11, 3837 2 of 22 sense, it can be easily said that location selection represents a typical multi-criteria decision-making (MCDM) problem [3][4][5][9][10][11].As an advanced and a widely used subdiscipline of operations research, MCDM basically represents a decision-making technique that can be used to evaluate a number of alternatives by taking into account multiple and usually conflicting criteria that may be quantitative or qualitative, and aims to provide support to the decision maker for making the choice between alternatives [9,[12][13][14]. In this framework, MCDM can be defined as the process of evaluating alternatives for the purpose of selection or ranking via utilizing a number of qualitative and/or quantitative criteria that have different measurement units [15].MCDM involves a set of techniques such as min-max, max-min, ELECTRE, PROMETHEE, TOPSIS, fuzzy TOPSIS, compromise programming, analytic hierarchy process (AHP), fuzzy AHP, data envelopment analysis, and goal programming, that can be used for comparing and prioritizing multiple alternatives and finally selecting the best-fit choice [16]. Among these techniques, for the solutions to location problems that contain vague and incomplete data and linguistic variables, fuzzy decision-making techniques have been attracting growing attention [12,17].On the other hand, it should be stated that these techniques are not suitable for spatial data [18]. In this context, with the advantage of handling both spatial and non-spatial data, geographic information systems (GIS) have been applied for solving location problems [18][19][20]. GIS represent computer-based tools that can be used for maintaining, managing, integrating and analyzing spatial data from different sources. GIS allow us to store, edit, manipulate and analyze geographically referenced data to generate interpretive maps and related statistics relevant for decision making [11,14,21]. In this sense, one could say that although MCDM and GIS have developed independently, they actually support one another and their combination generates more useful and reliable information that makes it possible to reach accurate decisions, especially in the case of location selection [11,14,22].Starting from this point of view, thi...

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Section: Problem Of Location Selection By the Fuzzy Topsis Methodsmentioning

confidence: 99%

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

Erdin

Akbaş

2019

Sustainability

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“…In the literature, the hesitant fuzzy TOPSIS method was taken into consideration by some studies. As an example, Cevik Onar et al [56], Yavuz et al [57], and Senvar et al [58] used this approach with the aim of selecting strategic decisions for different industries.…”

Section: Hesitant Fuzzy Topsismentioning

confidence: 99%

Analyzing the Global Risks for the Financial Crisis after the Great Depression Using Comparative Hybrid Hesitant Fuzzy Decision-Making Models: Policy Recommendations for Sustainable Economic Growth

2018

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The aim of this study is to analyze the effects of global risks on financial crises. For this purpose, five different outstanding crises after the Great Depression of 1929 are taken into the consideration. Additionally, four different dimensions are selected regarding global risk by considering the Global Risk Report. Moreover, the hesitant fuzzy DEMATEL, the hesitant fuzzy VIKOR, and the hesitant fuzzy TOPSIS methodologies are used to reach this objective. We concluded that, with respect to global risks, the industry-based dimension has the highest importance in comparison to other dimensions. In addition, we also identified that the 2010 European debt crisis and the 1982 Latin American debt crisis were the most influenced crises in terms of global risk. The main reason for this is that the macroeconomic problems such as high inflation and unemployment had negative impacts on the industries of these countries. Another important point is that the results of the hesitant fuzzy VIKOR and hesitant fuzzy TOPSIS models are quite different, but they are the most similar when the experts do not reach the consensus. This situation shows that this analysis is quite appropriate with respect to the hesitant approach. While considering these aspects, we recommended that countries should firstly focus on the solutions related to industry level problems in order to minimize the global risk. Owing to this issue, it can be more possible to reach sustainable economic growth in the world.

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“…However, in some real-life situations, the DM may not be able to precisely express the value of the ratings of alternatives with respect to criteria or else he/she uses linguistic expressions. In such situations, when evaluations are based on unquantifiable, incomplete, or unobtainable information, the DM may use other data formats, such as: interval numbers (Jahanshahloo et al 2006a;Yue 2011), fuzzy numbers (Chen 2000;Jahanshahloo et al 2006b), hesitant fuzzy sets (Senvar et al 2016), intuitionistic fuzzy sets (Boran et al 2009) and others. Recently, Roszkowska and Kacprzak (2016) have developed a new approach for TOPSIS in which information provided by the DM is represented in the form of ordered fuzzy numbers (OFNs) which are well suited to handle incomplete and uncertain knowledge.…”

Section: Introductionmentioning

confidence: 99%

An extended TOPSIS method based on ordered fuzzy numbers for group decision making

Kacprzak

2019

Artif Intell Rev

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Multiple criteria decision making methods have become very popular in recent years and are frequently applied in many real-life situations. One of the most commonly used is the Technique for Order Preference by Similarity to Ideal Solution. Its original version is based on the information provided by the decision maker as exact numerical values. However, in some real-life situations, the decision maker may not be able to precisely express the value of the ratings of alternatives with respect to criteria or else he/she uses linguistic expressions. In such situations he/she may use other data formats, such as: interval numbers, fuzzy numbers, ordered fuzzy numbers, hesitant fuzzy sets, intuitionistic fuzzy sets and other. On the other hand, the increasing complexity of the decision problems analysed makes it less feasible to consider all the relevant aspects of the problems by a single decision maker. As a result, many real-life problems are discussed by a group of decision makers. The aim of this paper and its main contribution is to present a new approach for ranking of alternatives for group decision making using the Technique for Order Preference by Similarity to Ideal Solution method based on ordered fuzzy numbers. This is an alternative to methods that use different forms of averages for the aggregation of the individual matrices into a collective matrix. In the proposed approach aggregation is not needed and all individual decision information of decision makers is taken into account in determining the ranking of alternatives and the selecting the best one. The key stage of this method is the transformation of the decision matrices provided by the decision makers into matrices of alternatives. A matrix corresponding to an alternative is composed of its assessments with respect to all criteria, performed by all the decision makers. A numerical example illustrates the proposed approach.

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Hospital Site Selection via Hesitant Fuzzy TOPSIS

Cited by 83 publications

References 19 publications

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

A Comparative Analysis of Fuzzy TOPSIS and Geographic Information Systems (GIS) for the Location Selection of Shopping Malls: A Case Study from Turkey

Analyzing the Global Risks for the Financial Crisis after the Great Depression Using Comparative Hybrid Hesitant Fuzzy Decision-Making Models: Policy Recommendations for Sustainable Economic Growth

An extended TOPSIS method based on ordered fuzzy numbers for group decision making

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