scholarly journals Evaluation of Combating Desertification Alternatives Using Promethee Model

2016 ◽  
Vol 8 (2) ◽  
pp. 1 ◽  
Author(s):  
Mohammad Hassan Sadeghravesh ◽  
Hassan Khosravi ◽  
Azam Abolhasani ◽  
Sahar Shekoohizadeghan
Keyword(s):  
Decision Making ◽  
Land Use Changes ◽  
Multiple Attribute Decision Making ◽  
Decision Matrix ◽  
Promethee Ii ◽  
Multiple Attribute ◽  
Combating Desertification ◽  
Extent Of Damage ◽  
Decision Making Model ◽  
Desertification Risk

According to the extent of damage, various effects and complexity of desertification process, selecting appropriate alternatives considering all effective desertification criterions is one of the main concerns of Iran in the field of natural resources. This can be effective in controlling, reclamation of disturbed lands and avoiding destruction areas at desertification risk. This paper tries to provide a systematic and optimal alternatives in a group decision-making model. For this aim, PROMETHEE II method was used for ranking desertification alternatives. At the first in the framework of Multiple Attribute Decision-making (MADM), normalized decision matrix was provided by  Delphi model. Then, to ease and accuracy in estimating the criteria preference and alternatives priority, the normalized decision matrix data were entered in Visual PROMETHEE software. Based on the results, the alternatives of prevention of unsuitable land use changes (A18), vegetation cover development and reclamation (A23) and modification of ground water harvesting (A31) with pure out ranking progress of =0.3660, 0.1909 and -0.0887 were selected as the main combating desertification altarnative in the study area, respectively. Therefore, it is suggested that the obtained results and ranking should be considered in projects of controlling and reducing the effects of desertification and rehabilitatyion of degraded lands plans.

scholarly journals An Interactive Data-Driven (Dynamic) Multiple Attribute Decision Making Model via Interval Type-2 Fuzzy Functions

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 584
Author(s):  
Baykasoğlu ◽  
Gölcük
Keyword(s):  
Decision Making ◽  
Historical Data ◽  
Multiple Attribute Decision Making ◽  
Data Driven ◽  
Decision Matrix ◽  
Multiple Attribute ◽  
Ranking Of Alternatives ◽  
Interval Type ◽  
Decision Making Model

A new multiple attribute decision making (MADM) model was proposed in this paper in order to cope with the temporal performance of alternatives during different time periods. Although dynamic MADM problems are enjoying a more visible position in the literature, majority of the applications deal with combining past and present data by means of aggregation operators. There is a research gap in developing data-driven methodologies to capture the patterns and trends in the historical data. In parallel with the fact that style of decision making evolving from intuition-based to data-driven, the present study proposes a new interval type-2 fuzzy (IT2F) functions model in order to predict current performance of alternatives based on the historical decision matrices. As the availability of accurate historical data with desired quality cannot always be obtained and the data usually involves imprecision and uncertainty, predictions regarding the performance of alternatives are modeled as IT2F sets. These estimated outputs are transformed into interpretable forms by utilizing the vocabulary matching procedures. Then the interactive procedures are employed to allow decision makers to modify the predicted decision matrix based on their perceptions and subjective judgments. Finally, ranking of alternatives are performed based on past and current performance scores.

scholarly journals Methods for MADM with Picture Fuzzy Muirhead Mean Operators and Their Application for Evaluating the Financial Investment Risk

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 6 ◽  
Author(s):  
Rui Wang ◽  
Jie Wang ◽  
Hui Gao ◽  
Guiwu Wei
Keyword(s):  
Decision Making ◽  
Fuzzy Numbers ◽  
Multiple Attribute Decision Making ◽  
Aggregation Operators ◽  
Investment Risk ◽  
Financial Investment ◽  
Multiple Attribute ◽  
Fuzzy Aggregation ◽  
Fuzzy Madm ◽  
Decision Making Model

In this article, we study multiple attribute decision-making (MADM) problems with picture fuzzy numbers (PFNs) information. Afterwards, we adopt a Muirhead mean (MM) operator, a weighted MM (WMM) operator, a dual MM (DMM) operator, and a weighted DMM (WDMM) operator to define some picture fuzzy aggregation operators, including the picture fuzzy MM (PFMM) operator, the picture fuzzy WMM (PFWMM) operator, the picture fuzzy DMM (PFDMM) operator, and the picture fuzzy WDMM (PFWDMM) operator. Of course, the precious merits of these defined operators are investigated. Moreover, we have adopted the PFWMM and PFWDMM operators to build a decision-making model to handle picture fuzzy MADM problems. In the end, we take a concrete instance of appraising a financial investment risk to demonstrate our defined model and to verify its accuracy and scientific merit.

scholarly journals Evaluation for Core Competence of Private Enterprises in Xuchang City Based on an Improved Dynamic Multiple-Attribute Decision-Making Model

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Jian-min Shen ◽  
Yao-guo Dang ◽  
Wei-jie Zhou ◽  
Xue-mei Li
Keyword(s):  
Decision Making ◽  
Core Competence ◽  
Multiple Attribute Decision Making ◽  
Private Enterprises ◽  
Multiple Attribute ◽  
Relational Degree ◽  
The Core Competence ◽  
Improved Model ◽  
Decision Making Model ◽  
Grey Relational

Because Deng’s grey relational degree is inconspicuous, Deng’s relational degree with an exponential function is first presented. Then, we demonstrate that improved Deng’s relational degree is more conspicuous than the original model. Then, we construct a multiple-attribute decision-making model, based on improved Deng’s relational degree with multiple stages, and a method for determining the weight of the index is also developed. Finally, the core competence of private enterprises in Henan province is analyzed, illustrating the validity and feasibility of the improved model.

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