Group decision making-based TODIM under linguistic aggregation majority additive operator
This paper proposes an extension of TODIM (interactive multi-criteria decision making ) under group decision making (GDM) using the Linguistic Aggregation Majority Additive (LAMA) operator . TODIM is an effective method in modelling experts' psychological behaviour in the decision - making p...
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| 主要な著者: | , , , |
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| フォーマット: | Conference or Workshop Item |
| 言語: | English |
| 出版事項: |
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.unisza.edu.my/4197/1/FH03-FIK-21-56529.pdf http://eprints.unisza.edu.my/4197/ |
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| 要約: | This paper proposes an extension of TODIM (interactive multi-criteria decision making ) under
group decision making (GDM) using the Linguistic Aggregation Majority Additive (LAMA)
operator . TODIM is an effective method in modelling experts' psychological behaviour in the
decision - making process. However, under the GDM, the method is based solely on the average of
all experts' judgments without any consideration to soft aggregation processes that include majority
and/or minority concepts. In this work, the LAMA operator is used to be integrated with TODIM -
GDM to aggregate the experts' opinions with respect to the majority concept under the linguistic
domain. This is to provide a greater flexibility in reaching a consensus instead of only considering
equally average using the classical averaging operators. Two linguistic representations, namely,
symbolic approach and 2-tuple linguistic approach for LAMA operator are proposed to be utilised in
the method. A numerical example in investment selection problem is provided to illustrate the
applicability of the proposed method. Finally, the comparison of these two linguistic approaches is
presented. The results show that LAMA under the 2-tuple linguistic approach is preferable to the
symbolic approach in case of there is a tie between alternatives in the final ranking. |
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