Evangelos Triantaphyllou

Bio: Evangelos Triantaphyllou is an academic researcher from Louisiana State University. The author has contributed to research in topics: Multiple-criteria decision analysis & Knowledge extraction. The author has an hindex of 27, co-authored 103 publications receiving 6532 citations. Previous affiliations of Evangelos Triantaphyllou include Kansas State University & Tulane University.

Multi-criteria Decision Making Methods: A Comparative Study

Abstract: List of Figures. List of Tables. Foreword. Preface. Acknowledgments. 1. Introduction to Multi-Criteria Decision Making. 2. Multi-Criteria Decision Making Methods. 3. Quantification of Qualitative Data for MCDM Problems. 4. Deriving Relative Weights from Ratio Comparisons. 5. Deriving Relative Weights from Difference Comparisons. 6. A Decomposition Approach for Evaluating Relative Weights Derived from Comparisons. 7. Reduction of Pairwise Comparisons Via a Duality Approach. 8. A Sensitivity Analysis Approach for MCDM Methods. 9. Evaluation of Methods for Processing a Decision Matrix and Some Cases of Ranking Abnormalities. 10. A Computational Evaluation of the Original and the Revised AHP. 11. More Cases of Ranking Abnormalities When Some MCDM Methods Are Used. 12. Fuzzy Sets and Their Operations. 13. Fuzzy Multi-Criteria Decision Making. 14. Conclusions and Discussion for Future Research. References. Subject Index. Author Index. About the Author.

Using the analytic hierarchy process for decision making in engineering applications: some challenges

Abstract: In many industrial engineering applications the final decision is based on the evaluation of a number of alternatives in terms of a number of criteria. This problem may become a very difficult one when the criteria are expressed in different units or the pertinent data are difficult to be quantified. The Analytic Hierarchy Process (AHP) is an effective approach in dealing with this kind of decision problems. This paper examines some of the practical and computational issues involved when the AHP method is used in engineering applications. Significance: In many engineering applications the final decision depends on the evaluation of a set of alternatives in terms of a number of decision criteria. This may be a difficult task and the Analytic Hierarchy Process seems to provide an effective way for properly quantifying the pertinent data. However, there are many critical issues that a decision maker needs to be aware of.

Ranking irregularities when evaluating alternatives by using some ELECTRE methods

Abstract: The ELECTRE II and III methods enjoy a wide acceptance in solving multi-criteria decision-making (MCDM) problems. Research results in this paper reveal that there are some compelling reasons to doubt the correctness of the proposed rankings when the ELECTRE II and III methods are used. In a typical test we first used these methods to determine the best alternative for a given MCDM problem. Next, we randomly replaced a non-optimal alternative by a worse one and repeated the calculations without changing any of the other data. Our computational tests revealed that sometimes the ELECTRE II and III methods might change the indication of the best alternative. We treat such phenomena as rank reversals. Although such ranking irregularities are well known for the additive variants of the AHP method, it is the very first time that they are reported to occur when the ELECTRE methods are used. These two methods are also evaluated in terms of two other ranking tests and they failed them as well. Two real-life cases are described to demonstrate the occurrence of rank reversals with the ELECTRE II and III methods. Based on the three test criteria presented in this paper, some computational experiments on randomly generated decision problems were executed to test the performance of the ELECTRE II and III methods and an examination of some real-life case studies are also discussed. The results of these examinations show that the rates of the three types of ranking irregularities were rather significant in both the simulated decision problems and the real-life cases studied in this paper.

Multi-Criteria Decision Making Methods

Abstract: With the continuing proliferation of decision methods and their variants, it is important to have an understanding of their comparative value. Each of the methods uses numeric techniques to help decision makers choose among a discrete set of alternative decisions. This is achieved on the basis of the impact of the alternatives on certain criteria and thereby on the overall utility of the decision maker(s). The difficulty that always occurs when trying to compare decision methods and choose the best one is that a paradox is reached, i.e., What decision-making method should be used to choose the best decision-making method? This problem is examined in Chapter 9.

A Sensitivity Analysis Approach for Some Deterministic Multi-Criteria Decision-Making Methods*

Abstract: Often data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights of the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology.

Thinking fast and slow.

Abstract: In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Best-worst multi-criteria decision-making method

Abstract: In this paper, a new method, called best-worst method (BWM) is proposed to solve multi-criteria decision-making (MCDM) problems. In an MCDM problem, a number of alternatives are evaluated with respect to a number of criteria in order to select the best alternative(s). According to BWM, the best (e.g. most desirable, most important) and the worst (e.g. least desirable, least important) criteria are identified first by the decision-maker. Pairwise comparisons are then conducted between each of these two criteria (best and worst) and the other criteria. A maximin problem is then formulated and solved to determine the weights of different criteria. The weights of the alternatives with respect to different criteria are obtained using the same process. The final scores of the alternatives are derived by aggregating the weights from different sets of criteria and alternatives, based on which the best alternative is selected. A consistency ratio is proposed for the BWM to check the reliability of the comparisons. To illustrate the proposed method and evaluate its performance, we used some numerical examples and a real-word decision-making problem (mobile phone selection). For the purpose of comparison, we chose AHP (analytic hierarchy process), which is also a pairwise comparison-based method. Statistical results show that BWM performs significantly better than AHP with respect to the consistency ratio, and the other evaluation criteria: minimum violation, total deviation, and conformity. The salient features of the proposed method, compared to the existing MCDM methods, are: (1) it requires less comparison data; (2) it leads to more consistent comparisons, which means that it produces more reliable results.

The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data

Abstract: 1. Introduction to text mining 2. Core text mining operations 3. Text mining preprocessing techniques 4. Categorization 5. Clustering 6. Information extraction 7. Probabilistic models for Information extraction 8. Preprocessing applications using probabilistic and hybrid approaches 9. Presentation-layer considerations for browsing and query refinement 10. Visualization approaches 11. Link analysis 12. Text mining applications Appendix Bibliography.