2016
DOI: 10.5815/ijisa.2016.05.04
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Sensitivity Analysis Using Simple Additive Weighting Method

Abstract: The output of a mu ltip le criteria decision method often has to be analyzed using some sensitivity analysis technique. The SAW MCDM method is commonly used in management sciences and there is a critical need for a robust approach to sensitivity analysis in the context that uncertain data is often present in decision models. Most of the sensitivity analysis techniques for the SAW method involve Monte Carlo simu lation methods on the initial data. These methods are computationally intensive and often require co… Show more

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Cited by 25 publications
(17 citation statements)
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“…Weighting (S-SAW) (Goodridge, 2016). Due to its simplicity, this sensitivity analysis method has a vast potential to be applied to other MCDM methods as an extension of the sensitivity analysis investigation.…”
Section: Multi Criteria Decision Making Methods Have Been Usedmentioning
confidence: 99%
“…Weighting (S-SAW) (Goodridge, 2016). Due to its simplicity, this sensitivity analysis method has a vast potential to be applied to other MCDM methods as an extension of the sensitivity analysis investigation.…”
Section: Multi Criteria Decision Making Methods Have Been Usedmentioning
confidence: 99%
“…The method involves a lot of decision-making criteria which usually described as Multi Criteria Decision Making (MCDM) method. According to Olson [7] and Goodridge performed a sensitivity analysis using the Simple Additive Weighting (SAW) MCDM method [8]. In his paper, the SAW method is extended to include an objective function which makes it easy to analyze the influence of specific changes in certain criteria values thus making easy to perform sensitivity analysis.…”
Section: Related Workmentioning
confidence: 99%
“…Weighing becomes an important issue to yield ranking such as the issue of weight update [6], new Entropy weight [7], Sensitivity-Simple Additive Weighting (S-SAW) [8], mutual weight [9]- [11].…”
Section: Related Workmentioning
confidence: 99%