Fuzzy group decision-making for facility location selection

Kahraman, Cengiz; Ruan, Da; Doğan, İbrahim

doi:10.1016/s0020-0255(03)00183-x

Cited by 469 publications

(234 citation statements)

References 39 publications

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“…Of the several algorithms, since fuzzy linguistic models permit the translation of verbal expressions into numerical values, MCDM methods based on fuzzy relations have been used quite successfully (Malczewski 2002;Kahraman et al 2003). Thus, in the first method, Saaty's (2000) AHP, a MCDM methodology in conjunction with fuzzy logic, was used to rank and prioritize the causative factors of fire risk in the study area.…”

Section: Fuzzy Ahp Methodsmentioning

confidence: 99%

Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

Eskandari

Miesel

2017

Geomatics, Natural Hazards and Risk

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This study was done to evaluate the efficiency of three methods to predict the high-risk areas for fire in District Three of Neka Zalemroud forests located in Mazandaran Province, Iran. The fuzzy analytic hierarchy process (fuzzy AHP) and the spatial correlation method were used to model fire risk in the study area. The Dong model was used to provide the fire risk map. Following the construction of fire risk maps using three methods, the map of actual fires was overlaid to validate the used methods. Then the area of the high-risk and very high-risk classes of each fire risk map within the perimeters of actual fires was calculated. Results showed that the high-risk areas in the fire risk map prepared by the fuzzy AHP and spatial correlation methods closely follow the actual fires. On the other hand, the high-risk areas in the fire risk map prepared by the Dong model showed only a moderate agreement with actual fire areas. The final results showed that the fuzzy AHP model (accuracy 0.8) and the spatial correlation model (accuracy 0.92) have the strongest ability to predict the fire high-risk areas in Hyrcanian forests of Iran, relative to the Dong model (accuracy 0.51).

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Section: Fuzzy Ahp Methodsmentioning

confidence: 99%

Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

Eskandari

Miesel

2017

Geomatics, Natural Hazards and Risk

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“…The decision variables are the number (n) and the location ) , ( Dasci and Verter [6] use some assumptions to solve problem (10). Their method ends to the result that given ) , ( y x as a facility location point, the best area as its respective service region is:…”

Section: Decision Variablesmentioning

confidence: 99%

“…forbidden regions) must be introduced in order to construct a reasonable model [7]. To this end, many concepts, tools and techniques of artificial intelligence such as fuzzy logic can be used to improve the implementation of numerous models in operations research [10,11,18,20].…”

Section: Introductionmentioning

confidence: 99%

“…The literature shows that the majority of the works in the area of facility location have used fuzzy theory to fuzzify the parameters of the model or have dealt with the facility location problem as a fuzzy multiple attribute decision making problem [2,3,10,16,24]. Wen and Kang [25], for example, consider fuzzy demands to construct some optimization models for facility location.…”

Section: Introductionmentioning

confidence: 99%

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Facility location via fuzzy modeling and simulation

Rezaei¹,

Zarandi²

2011

Applied Soft Computing

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This paper presents a continuous facility location model with fuzzy methodology. The developments concern mainly to some drawbacks in the initial model which takes it far from being used in practice. A fuzzy modeling method is proposed to estimate the required functions in the initial model. Structure identification in the proposed fuzzy modeling method is carried out using subtractive clustering, and parameter identification is conducted via some heuristics as well as an optimization problem.Furthermore, a simulation method along with some heuristic relations is used for implementation and evaluation of the modified model. Efficiency of the proposed method to fuzzy modeling as well as the proposed simulation method is presented by a numerical example.

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“…In this study, both the multiple-objective and multiple-attribute decision making techniques were utilized. Kahraman et al (2003) aimed to solve facility location problems using different solution approaches of fuzzy multi-attribute-decision-making. Chou et al (2008) proposed a fuzzy multi-criteria decision-making model for international tourist hotel location selection.…”

Section: Introductionmentioning

confidence: 99%

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

Tuzkaya

Gülsün

2008

Int. J. Environ. Sci. Technol.

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ABSTRACT:In this paper, the centralized return centers location evaluation problem in a reverse logistics network is investigated. This problem is solved via an integrated analytic network process-fuzzy technique for order preference by similarity to ideal solution approach. Analytic network process allows us to evaluate criteria preferences while considering interdependence between them. On the other hand, technique for order preference by similarity to ideal solution decreases the number of computational steps compared to simple analytic network process evaluation. An important characteristic of the centralized return centers location evaluation problem, vagueness, is adapted to the methodology via the usage of fuzzy numbers in the technique for order preference by similarity to ideal solution approach. Finally, a numerical example is given to demonstrate the usefulness of the methodology. The results indicate that, this integrated multi-criteria decision making methodology is suitable for the decision making problems that needs considering multiple criteria conflicting each other. Also, by using this methodology, the interdependences between the criteria may be considered for these kinds of problems in a flexible and systematic manner.

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Fuzzy group decision-making for facility location selection

Cited by 469 publications

References 39 publications

Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran

Facility location via fuzzy modeling and simulation

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

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