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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
|
2021
|
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.
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A New Procedure of Criteria Weight Determination Within the ARAS Method

86%
Ghram M., Moalla Frikha H.
Multiple Criteria Decision Making
|
2018
|
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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