Integration of fuzzy reasoning approach (FRA) and fuzzy analytic hierarchy process (FAHP) for risk assessment in mining industry

Verma, Shikha; Chaudhri, Sharad

doi:10.3926/jiem.948

Cited by 18 publications

(14 citation statements)

References 21 publications

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“…Se considera como el inicio formal de la lógica difusa en 1965, cuando el profesor Lotfi A. Zadeh, publicó su trabajo "Fuzzy Sets" [19]. El método FAHP ha sido aplicado a una diversidad de problemas como por ejemplo para determinar la percepción de la calidad de servicios hoteleros [20] o para determinar los indicadores de la actividad emprendedora en una universidad [21] o para la evaluación de riesgos en la industria minera [22]. FAHP es una alternativa para SIA porque podría evaluar el impacto social usando criterios de forma similar a AHP pero basado en la lógica difusa.…”

Section: A3: Método Fahpunclassified

Selección de un método para la evaluación del impacto social usando AHP

Delgado

Romero

2018

Rev. ECIPeru

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Selección de un método para la evaluación del impacto social usando AHP Selection of a method for social impact assessment using AHP Alexi Delgado1, 2, I. Romero1 1 Instituto Universitario de Investigación de Ingeniería del Agua y Medio Ambiente (IIAMA), Universitat Politècnica de València, España. 2 Universidad de Ciencias y Humanidades, Perú. DOI: https://doi.org/10.33017/RevECIPeru2015.0013/ Resumen El incremento de conflictos socio-ambientales durante la planificación o la ejecución de proyectos hacen que la evaluación del impacto social sea cada vez más necesaria y objetiva con el fin de prevenir posibles conflictos sociales. En este artículo realizamos un estudio para seleccionar la mejor alternativa metodológica disponible y aplicable a la evaluación del impacto social – social impact assessment (SIA) en proyectos y programas. La selección fue realizada aplicando la metodología del proceso analítico jerárquico - analytic hierarchy process (AHP). En este estudio se propusieron cuatro métodos alternativos para la evaluación del impacto social: Delphi, AHP, FAHP y agrupación Grey, los cuales fueron jerarquizados de acuerdo a los criterios de: Cuantificación, robustez y estandarización. Para realizar la selección usando AHP se contó con la participación de un panel de cuatro expertos seleccionados para este estudio. Los resultados mostraron que el mejor método para la evaluación del impacto social es la Evaluación grey complementado con el método Delphi y otros procedimientos cualitativos durante la fase de estudio de campo y recolección de información. Descriptores: Evaluación del impacto social, Proceso analítico jerárquico (AHP) Abstract Increase of socio-environmental conflicts during the planning or execution of projects makes social impact assessment is increasingly necessary and objective in order to prevent potential social conflicts. In this paper, we present a study to select the best alternative methodology available and applicable to social impact assessment (SIA) on projects and programs. The selection was conducted using the methodology of analytic hierarchy process (AHP). In this study four alternative methods for social impact assessment were proposed: Delphi, AHP, FAHP and Grey clustering, which were ranked according to criteria: Quantification, robustness and standardization. To make the selection using AHP, we provided of a panel of four experts selected for this study. The results showed that the best method for social impact assessment is the Grey clustering complemented with Delphi method and other qualitative procedures during field study and data collection. Keywords: Social Impact Assessment (SIA), Analytic Hierarchy Process (AHP).

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Section: A3: Método Fahpunclassified

Selección de un método para la evaluación del impacto social usando AHP

Delgado

Romero

2018

Rev. ECIPeru

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“…To decrease the uncertainty of the input attributes we apply fuzzy set theory i.e., triangular fuzzy numbers [15,16]. Application of the fuzzy set theory has found wide use for solving problems in the mining industry [17][18][19][20][21].…”

Section: The Relative Closenessmentioning

confidence: 99%

“…The optimal number of technological mining cuts is 4 and the technological model of the coal deposit composed of four mining cuts is represented by Figure 7. According to Equation (20) the input decision matrix composed of six criteria is as follows: (14)- (19) and represented in Table 7. The optimal number of technological mining cuts is 4 and the technological model of the coal deposit composed of four mining cuts is represented by Figure 7.…”

Section: Euclidean Distancementioning

confidence: 99%

Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms

Gligorić

Beljić

et al. 2016

Energies

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Abstract:The main aim of a coal deposit model is to provide an effective basis for mine production planning. The most applied approach is related to block modeling as a reasonable global representation of the coal deposit. By selection of adequate block size, deposits can be well represented. A block has a location in XYZ space and is characterized by adequate attributes obtained from drill holes data. From a technological point of view, i.e., a thermal power plant's requirements, heating value, sulfur and ash content are the most important attributes of coal. Distribution of attributes' values within a coal deposit can vary significantly over space and within each block as well. To decrease the uncertainty of attributes' values within blocks the concept of fuzzy triangular numbers is applied. Production planning in such an environment is a very hard task, especially in the presence of requirements. Such requirements are considered as target values while the values of block attributes are the actual values. To make production planning easier we have developed a coal deposit model based on clustering the relative closeness of actual values to the target values. The relative closeness is obtained by the TOPSIS method while technological clusters are formed by fuzzy C-mean clustering. Coal deposits are thus represented by multi-attribute technological mining cuts.

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“…In 2014, the order of preference by similarity to ideal solution (TOPSIS), a multi-criteria decision support method, was proposed to assess the risks of underground coal mines associated with human health and safety [31]. Verma and Chaudhri integrated the fuzzy reasoning technique and fuzzy AHP approaches for the assessment of the risk levels related to the risk factors in the mining industry [32]. Petrović et al proposed a model of the risk evaluation of mining equipment failure based on the fuzzy sets and analyzed the detectability, occurrence, and severity of the risk indicators [33].…”

Section: Introductionmentioning

confidence: 99%

Safety Risk Assessment of a Pb-Zn Mine Based on Fuzzy-Grey Correlation Analysis

Dong

Wei

et al. 2020

Electronics

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Improving safety management and risk evaluation methods is important for the global mining industry, which is the backbone of the industrial development of our society. To prevent any accidental loss or harm to human life and property, a safety risk assessment method is needed to perform the continuous risk assessment of mines. Based on the requirements of mine safety evaluation, this paper proposes the Pb-Zn mine safety risk evaluation model based on the fuzzy-grey correlation analysis method. The model is compared with the risk assessment model based on the fuzzy TOPSIS method. Through the experiments, our results demonstrate that the proposed fuzzy-grey correlation model is more sensitive to risk and has less effect on the evaluation results under different scoring attitudes (cautious, rational, and relaxed).

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Integration of fuzzy reasoning approach (FRA) and fuzzy analytic hierarchy process (FAHP) for risk assessment in mining industry

Cited by 18 publications

References 21 publications

Selección de un método para la evaluación del impacto social usando AHP

Selección de un método para la evaluación del impacto social usando AHP

Multi-Attribute Technological Modeling of Coal Deposits Based on the Fuzzy TOPSIS and C-Mean Clustering Algorithms

Safety Risk Assessment of a Pb-Zn Mine Based on Fuzzy-Grey Correlation Analysis

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