Aggregation Weights for Linguistic Hybrid Geometric Averaging Operator
This paper tries to point out that the aggregation weights in linguistic hybrid geometric averaging operator will dominate the final result of the ranking for alternatives. We examined the linguistic hybrid geometric averaging operator that was proposed by previous studies and found it contained s...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Operations research society of Taiwan
2017-09-01
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Series: | International Journal of Operations Research |
Subjects: | |
Online Access: | http://www.orstw.org.tw/ijor/vol14no4/IJOR2017_vol14_no4_p177_p185.pdf |
Summary: | This paper tries to point out that the aggregation weights in linguistic hybrid geometric averaging
operator will dominate the final result of the ranking for alternatives. We examined the linguistic hybrid geometric
averaging operator that was proposed by previous studies and found it contained several questionable results. The
major defect of the previous approach was that it failed to demonstrate two core factors: accuracy and speed, both of
which have been explicitly uncovered and discussed in the study. With previous work the pivotal and dominant element,
distribution of weights, in finding subjectively by decision maker of linguistic hybrid geometric averaging operators
for group decision-making problems, lacks solid foundation and is unjustified. Here we provide the mathematical
rationale and reliable advices, to point out that deficiency. In addition, we have detected and rectified some
redundancies of operational laws in the procedure of previous study due to the improper utilization of negative
operators. It certainly should be noted that the careless applications of those highly dependant operators may
significantly diminish the efficiency and performance of entire mechanism for decision making under fuzzy
environment. We develop an easy aggregation approach based on the arithmetic mean to solve the most favorable
alternative problem. A comprehensive numerical examination of 1296 tests supports our result. |
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ISSN: | 1813-713X 1813-7148 |