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An extension of the codas method based on interval rough numbers for multi-criteria group decision making

100%
Regaieg Cherif M., Moalla Frikha H.
Multiple Criteria Decision Making
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2021
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vol. 16
23-43
EN
This study aims to develop a new Interval Rough COmbinative Distance-based Assessment (IR CODAS) method for handling multiple criteria group decision making problems using linguistic terms. A single decision maker is unable to express his opinions or preferences on multiple criteria decisions, while a Multi-Criteria Group Decision Making MCGDM process ensures successful outcomes when handling greater imprecision and vagueness information. A real-life case study of risk assessment is investigated using our proposed IR-CODAS method to test and validate its application; a sensitivity analysis is also performed.
2
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An intuitionistic fuzzy extension of the codas-sort method

100%
Ouhibi A., Moalla Frikha H.
Multiple Criteria Decision Making
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2021
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vol. 16
110-121
EN
Currently, an important issue in multi-criteria decision-making (MCDM) problems are vagueness and lack of precision of decision- -making information because of insufficient data and incapability of the decision maker to process the information. Intuitionistic fuzzy sets (IFS) are a solution to eliminate the vagueness and the uncertainty. While fuzzy sets (FS) deal with ambiguity and vagueness problem, IFSs have more advantages. Moreover, the CODAS-SORT method cannot handle the uncertainty and ambiguity of information provided by human judgments. The aim of this study is to develop an IF extension of CODAS-SORT combining this method with the IFS theory. To achieve this, we use the fuzzy weighted Euclidean distance and fuzzy weighted Hamming distance instead of the crisp distances. A case study of a supplier selection assessment is used to clarify the details of our proposed method.
3
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A new approach for criteria weight elicitation of the ARAS-H method

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Ghram M., Moalla Frikha H.
Multiple Criteria Decision Making
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2021
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vol. 16
89-109
EN
Criteria weight inference is a crucial step for most of multi-criteria methods. However, criteria weights are often determined directly by the decision-maker (DM) which makes the results unreliable. Therefore, to overcome the imprecise weighting, we suggest the use of the preference programming technique. Instead of obtaining criteria weights directly from the DM, we infer them in a more objective manner to avoid the subjectivity and the unreliability of the results. Our aim is to elicit the ARAS-H criteria weights at each level of the hierarchy tree via mathematical programming, taking into account the DM’s preferences. To put it differently, starting from preference information provided by the DM, we proceed to model our constraints. The ARAS-H method is an extension of the classical ARAS method for the case of hierarchically structured criteria. We adopt a bottom-up approach in order to elicit ARAS-H criteria weights, that is, we start by determining the elementary criteria weights (i.e. the criteria at the lowest level of the hierarchy tree). The solution of the linear programs is obtained using LINGO software. The main contribution of our criteria weight elicitation procedure is in overcoming imprecise weighting without excluding the DM from the decision making process.
4
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Hierarchical Structuring for the Olive Trees Irrigation Problem in Tunisia

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Daoud Ben Amor W., Moalla Frikha H.
Multiple Criteria Decision Making
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2018
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vol. 13
29-55
EN
The problem of choosing the best type of water for the irrigation of olive trees is one of the decisions that have a crucial impact on the water resource management. To solve this problem, we propose a multi-expert approach, implying several quantitative and qualitative criteria and combining the AHP method and Shannon’s entropy probability method. First, we use the AHP method to calculate all criteria weights for the various hierarchical levels as well as weights of the alternatives. Using the results obtained, we rank the types of water according to four experts. However, the data supplied by the experts are contradictory. We therefore combine these results according to the experts’ importance. We used Shannon’s entropy to determine the importance degree of each expert, to aggregate the results. The proposed approach showed that using well water was selected as the best for irrigation. Reuse of treated wastewater was classified as second, followed by desalinated brackish water and, next, by desalinated seawater.
5
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The multicriteria group decision making flowsort method under uncertainty

100%
Remadi F. D., Moalla Frikha H.
Multiple Criteria Decision Making
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2021
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vol. 16
122-139
EN
Crisp values are insufficient to model real-life situations and imprecise ideas are frequently represented in multicriteria decision aid analysis. In fact, it is difficult to treat the evaluation criteria precisely and to fix exact preferences rating. The triangular intuitionistic fuzzy numbers succeeded to treat this kind of ambiguity in a great deal of research than other forms of fuzzy representation functions. The field of sorting issues is an active research topic in the multiple criteria decision aid (MCDA). This study extended one of the sorting methods, FLOWSORT, for solving multiple criteria group decision-making problems. This extension described the preferences rating of alternatives as linguistic terms which can be easily expressed in triangular intuitionistic fuzzy numbers. To validate our extension, an illustrative example as well as an empirical comparison with other multi-criteria decision making methods is presented.
6
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A New Procedure of Criteria Weight Determination Within the ARAS Method

100%
Ghram M., Moalla Frikha H.
Multiple Criteria Decision Making
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2018
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vol. 13
56-73
EN
In most of multicriteria aggregation methods, we need to elicit parameters that are generally determined directly by the decision-maker (DM). Direct assigning of parameters and criteria weights presents a crucial and difficult step in the decision-making process. However, this kind of information is too subjective and may affects the reliability of the results. To overcome this issue, we suggest a weighting method based on mathematical programming to incorporate the DM’s preferences indirectly within the ARAS method.
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