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A NEW COMBINATIVE DISTANCE-BASED ASSESSMENT(CODAS) METHOD FOR MULTI-CRITERIA DECISION-MAKING
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2016
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Mathematical ProgrammingEngineeringSimilarity MeasureRange SearchingMultiple-criteria Decision AnalysisProgram EvaluationOperations ResearchFuzzy Multi-criteria Decision-makingData ScienceData MiningUncertainty QuantificationManagementMulti-criteria Decision MakingSystems EngineeringMulticriteria EvaluationCombinatorial OptimizationDecision TheoryTransportation EngineeringStatisticsReliabilityFuzzy LogicCodas MethodComputer ScienceMcdm ProblemsMcdm MethodsDecision Science
Decision making is crucial for success in information‑intensive fields, yet real‑world problems involve many factors, making complex decisions difficult and necessitating multi‑criteria decision‑making methods. This paper introduces a new combinative distance‑based assessment (CODAS) method for handling multi‑criteria decision‑making problems. CODAS evaluates alternatives by computing Euclidean and Taxicab distances from the negative‑ideal point, ranking alternatives with larger distances as more desirable, and demonstrates the process through numerical examples and sensitivity analysis against existing MCDM methods. The analyses indicate that CODAS is efficient and yields stable results.
A key factor to attain success in any discipline, especially in a field which requires handling large amounts of information and knowledge, is decision making. Most real-world decision-making problems involve a great variety of factors and aspects that should be considered. Making decisions in such environments can often be a difficult operation to perform. For this reason, we need multi-criteria decision-making (MCDM) methods and techniques, which can assist us for dealing with such complex problems. The aim of this paper is to present a new COmbinative Distance-based ASsessment (CODAS) method to handle MCDM problems. To determine the desirability of an alternative, this method uses the Euclidean distance as the primary and the Taxicab distance as the secondary measure, and these distances are calculated according to the negative- ideal point. The alternative which has greater distances is more desirable in the CODAS method. Some numerical examples are used to illustrate the process of the proposed method. We also perform a comparative sensitivity analysis to examine the results of CODAS and compare it by some existing MCDM methods. These analyses show that the proposed method is efficient, and the results are stable.