Table Velasquez, Patrick T. Hester, 2013).Capable of handling multipleinputs

Table 2: Overview of Few Commonly Used Multi-Criterion Decision Making Techniques Method Description Strengths WeaknessSimple Additive Weighting method (SAW)SAW is “a value function is established based on a simple addition of scores that represent the goal achievement undereach criterion, multiplied by the particular weights” (Qin, Huang, Chakma, Nie, & Lin, 2008).Ability to compensate amongcriteria; intuitive to decisionmakers; calculation is simpledoes not require complexcomputer programs.Estimates revealed do not alwaysreflect the real situation; resultobtained may not be logical.Analytical Hierarchy Process (AHP)AHP is “a theory of measurement through pairwise comparisons and relies on the judgments of experts to derive priority scales” (Mark Velasquez, Patrick T. Hester, 2013).Easy to use; scalable; hierarchystructure can easily adjust to fitmany sized problems; not dataintensive.Problems due to interdependencebetween criteria and alternatives; canlead to inconsistencies betweenjudgment and ranking criteria; rankreversal.Technique for Order Preference by Similarity to Identical Solution (TOPSIS)Yoon and Hwang had proposed the TOPSIS method in the 1980s for the first time. TOPSIS is “an approach to identify an alternative which is closest to the ideal solution and farthest to the negativeideal solution in a multi-dimensional computing space”(Qin et al., 2008).It gives a solution which not only close to the ideal solution but also farthest from the hypothetical worst solution.TOPSIS does not consider uncertainty in weightings.DataEnvelopmentAnalysis (DEA)DEA uses a linear programming technique to measure the relative efficiencies of alternatives. It rates the efficiencies of alternatives against each other, with the most efficient alternative having a rating of 1.0, with all other alternatives being a fraction of 1.0(Mark Velasquez, Patrick T. Hester, 2013).Capable of handling multipleinputs and outputs; efficiencycan be analysed and quantified.Does not deal with imprecise data;assumes that all input and output areexactly known19Fuzzy Set TheoryFuzzy set theory is an extension of classical set theory that “allows solving a lot of problems related to dealing theimprecise and uncertain data” (Balmat, Lafont, Maifret, & Pessel, 2011) .Allows for imprecise input;considers insufficientinformation.Difficult to develop; can requirenumerous simulations before use.Goal ProgrammingGoal Programming is a pragmatic programming method that is able to choose from an infinite number of alternatives (Mark Velasquez, Patrick T. Hester, 2013).Capable of handling large-scaleproblems; can produce infinitealternatives.It’s ability to weight coefficients;typically needs to be used incombination with other MCDMmethods to weight coefficients.

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