Please use this identifier to cite or link to this item: http://hdl.handle.net/11023/3049
Title: Analysis of Alternatives and Performance Evaluation Using a New OWA Operator based on the Laplace Distribution
Author: Mohammed, Emad
Advisor: Far, Behrouz
Naugler, Christopher
Keywords: Education--Health;Education--Industrial;Business Administration--Management;Health Care Management;Artificial Intelligence;Computer Science
Abstract: Analysis of Alternatives (AoA) is an assessment of operational effectiveness, appropriateness, cost, and risk associated with alternative solutions to specific problem requirements. Decision makers can utilize the AoA outcome to support their informed decisions that favor a specific alternative. Multiple criteria decision-making (MCDM) denotes the act of choosing, implementing, and applying a specific course of action to solve problems based on the AoA result of the multiple criteria alternatives. An intrinsic characteristic of the criteria is their conflicting nature, i.e., some criteria are more appealing than others for different decision makers, and thus, the selection process of the best alternative is vastly dependent on the decision makers’ preferences. This introduces discrepancy in the AoA process, which results from systematic errors introduced by the decision makers. This is common in a typical group decision-making scenario where many individuals are involved in the decision process, and thus, a method to aggregate the different evaluation viewpoints is mandatory. The ordered weighted averaging (OWA) operator is a mapping function that is used to aggregate different viewpoints. This thesis describes a new method to calculate the weight vector of the OWA operator based on the Laplace distribution. The proposed OWA operator is a new method for AoA to minimize discrepancy in alternative assessment, e.g., disagreement on the weight vector that leads to higher scores for the appealing criteria and smaller scores for the less interesting ones. The proposed OWA operator assigns smaller weights to both the higher and smaller scores, and thus, reduces the discrepancy in the AoA process. To prove the usefulness of the proposed operator, the calculated score is utilized in machine learning models as an explanatory variable for regression and classification problems and the results are compared to other OWA operators. The proposed OWA operator outperforms other operators in a breast cancer classification problem with an accuracy of 99.71%. Furthermore, a new model based on the calculated score and the Z-score is proposed for alternatives performance evaluation. The results of this method are illustrated using a case study for used cars performance ranking and evaluation with sensitivity analysis.
URI: http://hdl.handle.net/11023/3049
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