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

Abstract

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...

Location Optimization of CR Express International Logistics Centers

“…The fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was used to select the optimal locations of shopping malls in Turkey [26]. This method selects the alternatives that have the shortest distance from the positive ideal solution and the farthest distance from the negative ideal solution at the same time [27,28].…”

A Consensus Measure of Expert Judgment in the Fuzzy TOPSIS Method

“…Aggregation of expert assessments on criteria in accordance with Equation (1) was also used in [39][40][41], where as part of creating hybrid approaches, the fuzzy TOPSIS method was combined with the Fuzzy AHP method. Examples of other fuzzy TOPSIS implementations using the technique of average value Shown in Equations (1) and (2) with TrFNs or trapezoidal fuzzy numbers (TFNs) were published in [42][43][44].…”

“…An important disadvantage of both techniques is the fact that the agreement degree of expert opinions is not taken into account (expert consensus). average value Shown in Equations (1) and (2) with TrFNs or trapezoidal fuzzy numbers (TFNs) were published in [42][43][44]. Another way to aggregate expert assessments and criteria weights expressed using TFNs, evaluation = , , , , and weight = , , , was presented by Chen et al [11].…”

Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling

“…The processing functions include transforming, analyzing, aggregating, and generalizing. GIS software allows spatial analysis using a variety of methods, the most popular include: overlay analysis, spatial interpolation, spatial data exploration, hot spot analysis, or kernel density estimation; this type of research finds application in a number of areas, e.g., bathymetry [29], spatial planning [30], location selection [31,32], environmental protection and disaster reduction capability [33][34][35][36][37], spatial and distribution patterns [38][39][40][41], demography [42], as well as many others. Using GIS software, the data can be visualized using many cartographic visualization methods, such as: cartogram, cartodiagram, range method, dot map, etc.…”