A. Çagri Tolga

An Alternative Ranking Approach and Its Usage in Multi-Criteria Decision-Making

In the process of fuzzy decision-making, ranking of fuzzy numbers is a necessity. The types of fuzzy numbers are triangular, trapezoidal, and L-R type. In the literature, there are many methods developed for ranking fuzzy numbers. These methods may produce different ranking results. Many of these methods necessitate graphical representations, complex and tedious calculations. The method developed in this paper has some advantages with respect to the other methods in both graphical representations and calculations. Applicability of the proposed method to multi-criteria decision-making methods, i.e. fuzzy scoring, fuzzy AHP and fuzzy TOPSIS methods, is shown in the paper.

A Fuzzy Multi-Criteria Decision Analysis Approach For Retail Location Selection

This paper proposes fuzzy multi-criteria decision-making approach integrated with fuzzy real option value theory. The applicability of the proposed method was shown on a real-world supermarket location selection problem. Based on the interviews with the experts, the evaluation criteria for retail location selection were identified. Then the network for fuzzy analytic network process (ANP) method was constructed. The fuzzy real option value for each alternative was calculated and used in the proposed approach as the representative of the financial dimension. Finally, the preference ranking of alternatives and the relative importance of the criteria were obtained. The significant contribution of the proposed approach is that it integrates the financial dimension (FROV) of the location problem methodologically with the multi-criteria characteristic (FANP) of the problem. Another importance of this study is the first usage of real options valuation in the area of location selection science.

Evaluation of Knowledge Management Tools by Using An Interval Type-2 Fuzzy TOPSIS Method

AbstractKnowledge management (KM) systems can provide businesses a wide range of advantages and efficiency improvements. Increasing competition forces companies to seek new ways to streamline their processes and manage their information and knowledge better, leading to increased demand for KM solutions. Considering various needs of organizations and diverse features of available KM alternatives, choosing the most suitable KM tool is an important decision for businesses. The contribution of this paper to the KM literature is a KM evaluation framework for decision makers to compare available KM products of different vendors by first identifying relevant evaluation criteria and then proposing a group decision making framework using the Interval Type-2 TOPSIS technique. This method has more flexibility in handling uncertainties compared to the Type-1 fuzzy sets and enables decision makers to effectively analyze, compare and select the most appropriate KM tools. The framework is also used in a case study for t...

Fuzzy Investment Planning and Analyses in Production Systems

Investment planning is a part of investment analysis that contains real investments, such as machines, lands, a new plant, a new ERP system implementation etc. Investment analysis concerns evaluation and comparison of the investment projects. In the planning phase timing is the important point to execute the project. A production system is an aggregation of equipment, people and procedures to perform the manufacturing operations of a company. Production systems can be divided into categories named facilities and manufacturing support systems. The facilities of the production system consist of the factory, the equipment, and the way the equipment is organized. In this chapter, the components of investment planning are given. Then, the fuzziness of the investment is presented. Fuzzy present worth, fuzzy annual worth, fuzzy rate of return analysis, fuzzy B/C ratio, fuzzy replacement analysis and fuzzy payback period techniques -performed in this chapter- are fuzzy investment analysis techniques. At the last section application of these techniques are subjected.

Fuzzy real option value integrated fuzzy ANP method for location selection problems

Location selection decisions are irreversible for any type of enterprise. Making the right choice is vital. In this work, at the beginning, criteria for location selection in retail sector are determined. Then because of the dependency among criteria we choose ANP method as multi-criteria decision aid. The data are ambiguous and the nature of the problem is dynamic, hence fuzzy ANP is preferred. As the measurement technique of the financial criterion, we used the fuzzy real option value that calculates the risky side of the problem. Fuzzy trinomial lattice method without any future competitor is offered as the solution procedure for fuzzy real option value through its impact on efficiency and accuracy.

Fuzzy Probability Theory I: Discrete Case

This chapter introduces the underlying theory of Fuzzy Probability and Statistics related to the differences and similarities between discrete probability and possibility spaces. Fuzzy Probability Theory for Discrete Case starts with the fundamental tools to implement an immigration of crisp probability theory into fuzzy probability theory. Fuzzy random variables are the initial steps to develop this theory. Different models for fuzzy random variables are designated regarding the fuzzy expectation and fuzzy variance. In order to derive the observation related to fuzzy discrete random variables, a brief summary of alpha-cuts is introduced. Furthermore, essential properties of fuzzy probability are derived to present the measurement of fuzzy conditional probability, fuzzy independency and fuzzy Bayes theorem. The fuzzy expectation theory is studied in order to characterize fuzzy probability distributions. Fuzzy discrete distributions; Fuzzy Binomial and Fuzzy Poisson are introduced with different examples. The chapter is concluded with further steps in the discrete case.

Fuzzy Probability Theory II: Continuous Case

Continuous probability density functions are widely used in various domains. The characterization of the fuzzy continuous probability theory is similar to the discrete case. However, the possibility space is continuous and the integration between the minimum and the maximum values would set the fuzzy probability through the alpha-cuts. In this chapter, the foundations of fuzzy probability and possibility theory are described for the continuous case. A brief introduction summarized the key concepts in this area with recent applications. The expectation theory is interpreted using the relationship with fuzzy continuous random variables. Fuzzy continuous applications are enriched with different probability density functions. Therefore, fundamental distributions are detailed within their uses and their properties. In this chapter, fuzzy uniform, fuzzy exponential, fuzzy laplace, fuzzy normal and fuzzy lognormal distributions are examined. Several examples are given for the use of these fuzzy distributions regarding the fuzzy interval algebra. Finally, the future suggestions and applications are discussed in the conclusion.

A multi-stage new product development using fuzzy Type-2 sets in a real option valuation

Introducing new products is a key factor for the success and survival of companies. Managers need to examine every possibility that can occur during the life span of their new product and focus on the right strategy. Even then, launching a new product is highly risky due to uncertainties of the market and competitors. This study presents a proposal of real option evaluation through fuzzy logic for a new product development project in a retail banking market with Type-2 fuzzy sets. A multi-stage new product development is used to have a better understanding of the long term success of the project. One of the real options evaluation technique is binomial lattice model. This technique is mostly suitable for managerial flexibility decisions. In this study different from the literature Binomial Lattice method and Type-2 fuzzy sets are combined to cope with uncertainties of the practice.

EVALUATION OF MEDICAL DEVICES USING FUZZY TOPSIS WITH TYPE-2 FUZZY NUMBERS

The evaluation of medical imaging devices is a critical issue for both biomedical engineers and health-care investors. This study proposes a new technique to assess common medical imaging devices using type-2 fuzzy multi-criteria decision making approach. The evaluation criteria were characterized by the interviews with the experts. A Gaussian type-2 Fuzzy membership function was assigned for each interval of the evaluation. TOPSIS algorithm was applied to our system using type-2 Fuzzy numbers. The results were classified with the Wu and Mendel’s ranking method. The ranking of device alternatives highlighted the accurate order of future imaging technologies with the fuzzy behavior of medical investments in conjunction with the requirements of the clinicians and the engineers.