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2 Operations Research and Decisions

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The extention rank ordering criteria weighting methods in fuzzy enviroment

100%

ROSZKOWSKA E.

nr 2

91-114

Weight elicitation is an important part of multi-criteria decision analysis. In real-life decision-making problems precise information is seldom available, and providing weights is often cognitively demanding as well as very time- and effort-consuming. The judgment of decision-makers (DMs) de-pends on their knowledge, skills, experience, personality, and available information. One of the weights determination approaches is ranking the criteria and converting the resulting ranking into numerical values. The best known and most widely used are rank sum, rank reciprocal and centroid weights tech-niques. The goal of this paper is to extend rank ordering criteria weighting methods for imprecise data, especially fuzzy data. Since human judgments, including preferences, are often vague and cannot be expressed by exact numerical values, the application of fuzzy concepts in elicitation weights is deemed relevant. The methods built on the ideas of rank order techniques take into account imprecise infor-mation about rank. The fuzzy rank sum, fuzzy rank reciprocal, and fuzzy centroid weights techniques are proposed. The weights obtained for each criterion are triangular fuzzy numbers. The proposed fuzzy rank ordering criteria weighting methods can be easily implemented into decision support systems. Numerical examples are provided to illustrate the practicality and validity of the proposed methods

Decision-makers’ behavioral characteristics and multiple criteria decision aiding. Impact of decision-making style and experience on methods’ use, evaluation, and recommendation

51%

WACHOWICZ T. , ROSZKOWSKA E. , FILIPOWICZ-CHOMKO M.

nr 3

287-323

The study aims to identify relationships among selected behavioral characteristics of decision-makers (DMs), i.e., experience in making complex decisions, decision-making style, and ability to use various multiple criteria decision-aiding (MCDA) methods coherently, and their impact on the evaluation of the latter functionality and recommendations for future use. The relationships were verified using experimental data through a structural equation model (SEM) and cluster analysis for three MCDA methods, i.e., AHP, SMART, and TOPSIS. One of the strongest effects identified by SEM was observed between coherence in methods’ use and the DM’s opinion on their functionality. DM’s satisfaction and future willingness to use MCDA tools are related to the positive experience gained from using these tools in advance. Decision-making styles shape method selection, with TOPSIS favored by highly experienced DMs, SMART by highly rational, and AHP by those with low experience and a rational approach.