Reza Ardakanian

A fuzzy-stochastic OWA model for robust multi-criteria decision making

All realistic Multi-Criteria Decision Making (MCDM) problems face various kinds of uncertainty. Since the evaluations of alternatives with respect to the criteria are uncertain they will be assumed to have stochastic nature. To obtain the uncertain optimism degree of the decision maker fuzzy linguistic quantifiers will be used. Then a new approach for fuzzy-stochastic modeling of MCDM problems will be introduced by merging the stochastic and fuzzy approaches into the OWA operator. The results of the new approach, entitled FSOWA, give the expected value and the variance of the combined goodness measure for each alternative. Robust decision depends on the combined goodness measures of alternatives and also on the variations of these measures under uncertainty. In order to combine these two characteristics a composite goodness measure will be defined. The theoretical results will be illustrated in a watershed management problem. By using this measure will give more sensitive decisions to the stakeholders whose optimism degrees are different than that of the decision maker. FSOWA can be used for robust decision making on the competitive alternatives under uncertainty.

Sensitivity Analysis of the OWA Operator

The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for the OWA. These two methods will be first analyzed in detail by using sensitivity analysis on the outputs of the OWA with respect to the optimism degree of the decision maker, and then the two methods will be compared. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree, and, therefore, it has better computation efficiency. Since maximizing the combined goodness measure and minimizing its sensitivity to optimism degree are conflicting objectives, a new composite measure of goodness will be defined to have more reliability in obtaining optimal solutions. The theoretical results will be illustrated in a water resources management problem.

Fuzzy Multiple Attribute Decision Making on Water Resources Projects Case Study: Ranking Water Transfers to Zayanderud Basin in Iran

Abstract In this study, in order to create a decision-making model on water resources projects, a hierarchy of criteria has been developed by public participation. The Value Management methodology has been used for extraction of the effective criteria and attributes in the scope of Integrated Water Resources Management (IWRM). The hierarchy is generic for water resources management in the Islamic Republic of Iran. Since the evaluations of alternatives with respect to some attributes are uncertain and vague, fuzzy set theory has been used. By merging fuzzy set theory and multi-attribute decision-making a new Decision Support System (DSS), namely FDM, has been developed to compare different alternatives. As an innovation, FDM accepts evaluations of alternatives with respect to the attributes as crisp variables, fuzzy variables, and linguistic variables. FDM embodies an expert system whose duty is to choose an appropriate method among the SAW, Fuzzy SAW, TOPSIS or Fuzzy TOPSIS based on the characteristics of the problem. The central and Southeastern regions of Iran are considered arid regions, suffering from water shortages. In this paper, water transfers to the Zayanderud basin in Iran have been modeled by FDM. Successful application of this DSS in this study allows for its application by water authorities in other case studies.

Obtaining robust decisions under uncertainty by sensitivity analysis on OWA operator

The successful design and application of the ordered weighted averaging (OWA) method as a decision making tool depends on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability methods which give different behavior patterns for OWA. These methods will be compared by using sensitivity analysis on the outputs of OWA with respect to the optimism degree of the decision maker. The theoretical results are illustrated in a water resources management problem. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the minimal variability method. However, in using the minimal variability method, the OWA has a linear behavior with respect to the optimism degree and therefore it has better computation efficiency. A simulation study is also reported in this paper, where the dependence of the optimal decision on the uncertainty level is examined. Also based on obtained sensitivity measure, a new combined measure of goodness has been defined to have more reliability in obtaining optimal solutions

Sensitivity analysis of an information fusion tool : OWA operator

The successful design and application of the Ordered Weighted Averaging (OWA) method as a decision making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the Fuzzy Linguistic Quantifiers approach and the Minimal Variability method which give different behavior patterns for OWA. These methods will be compared by using Sensitivity Analysis on the outputs of OWA with respect to the optimism degree of the decision maker. The theoretical results are illustrated in a water resources management problem. The Fuzzy Linguistic Quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the Minimal Variability method. However, in using the Minimal Variability method, the OWA has a linear behavior with respect to the optimism degree and therefore it has better computation efficiency.