Multi Criteria Decision Making Using Normalized 3 – Polar ELECTRE I and Fuzzy 3 – Polar Dombi Arithmatic AOs (Case Study of Determining Manufacture Location)

Ricky Haryanto (Mathematics Department, Faculty of Applied Science and Technology Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
Sugiyarto Surono (Mathematics Department, Faculty of Applied Science and Technology Universitas Ahmad Dahlan, Yogyakarta, Indonesia)

EKSAKTA: Journal of Sciences and Data Analysis
VOLUME 3, ISSUE 1, February 2022

The m-Polar fuzzy set is a set that not only overcomes data ambiguity, but can also handle multi-polar, multi-attribute, and multi-criteria information. The m-Polar fuzzy set is useful in describing uncertainty in multi-attribute decision-making. One of the techniques used in decision-making is the ELECTRE I method. The ELECTRE I method plays a role in conducting pairwise comparisons between alternatives given by the decision-maker, where alternatives, criteria, and weights are given by the decision-maker. Furthermore, the ranking results from the ELECTRE I method will be compared with the mF Dombi Weighted Averaging (m-FDWA) aggregation operator with the help of the arithmetic operator. The purpose of this study was to compare the ranking results of the mF ELECTRE I, and the normalized and non-normalized m-FDWA arithmatic methods. The data used is secondary data related to site selection for global manufacturing with 20 alternative countries (country) and 8 criteria. The results showed that the best alternative to the normalized mF ELECTRE I and m-FDWA methods was country 14. While the m-FDWA arithmetic method without normalization resulted in country 3 as the best alternative. The effectiveness test was applied to m-FDWA arithmetic method, both normalized and without normalization to test the validity of the model so that it can be seen that normalization does not affect the validity of the model.