İrem Uçal Sarı

Fuzzy analytic hierarchy process with interval type-2 fuzzy sets

The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1s, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

Interval Type-2 Fuzzy Capital Budgeting

Discounted cash flows methods are very popular in justifying investments. Uncertainty in investment parameters always exists and is inevitable. The fuzzy set theory is capable to capture this uncertainty through the membership functions of these parameters. However, ordinary fuzzy sets are criticized for having one single membership value for each certain parameter value. To remove this criticism, type-2 fuzzy sets were proposed. In this paper, we developed interval type-2 fuzzy capital budgeting techniques by using both triangular and trapezoidal interval type-2 fuzzy sets. The developed techniques are interval type-2 fuzzy net present value analysis, interval type-2 fuzzy future value analysis, and interval type-2 fuzzy equivalent uniform annual value analysis. These techniques are applied to a part of a real life investment project. The results show that the techniques produce consistent outputs, which means that the developed techniques can be used interchangeably.

Fuzzy Inference Systems for Disaster Response

Disaster management is extremely important in today’s world, which is defined as the organization and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters. Disaster response is one of the critical stages of disaster management, which necessitates spontaneous decision making when a disaster occurs. Fuzzy inference systems are very suitable for such decision making environments since the inputs and outputs of disaster events cannot be sharply defined. This chapter describes potential applications of fuzzy inference systems in disaster response.

RISK GOVERNANCE OF URBAN RAIL SYSTEMS USING FUZZY AHP: THE CASE OF ISTANBUL

The urban rail system in Istanbul carries in total more than 700.000 passengers per a day on different types of lines which require well organized risk governance. This paper evaluates the urban rail systems in Istanbul under different risk factors using Fuzzy Analytic Hierarchy Process (FAHP) to uncover the critical risk criteria of these systems and to make a multi-criteria evaluation of existing rail systems for the assignment of the scarce resources. Linguistic variables are used in the pairwise comparisons of criteria and alternatives. The risk factors considered are regional criticality, line characteristics, line safety and station structure. The evaluation results imply that the most risky critical urban rail system in Istanbul is the subway line from Sishane to Darussafaka.

Fuzzy Economic Analysis Methods for Environmental Economics

Environmental economics is an area of economics that studies the financial impact of environmental policies to determine the theoretical or empirical effects of these policies on the economy. In this chapter, engineering economy techniques are developed under fuzziness to be employed in environmental problems. Ordinary fuzzy sets, type-2 fuzzy sets , intuitionistic fuzzy sets , and hesitant fuzzy sets are handled in the development of fuzzy engineering economy analyses. For each of these fuzzy sets, an application related to environmental economics is given.

Multicriteria Environmental Risk Evaluation Using Type II Fuzzy Sets

In this paper, interval type-2 fuzzy TOPSIS is used for environmental risk evaluation. By type-2 fuzzy sets we can consider the footprint of uncertainty in the membership functions. A multicriteria selection among the treatment alternatives of hazardous waste management is made. An application is presented.

Economic Analysis of Municipal Solid Waste Collection Systems Using Type-2 Fuzzy Net Present Worth Analysis

Solid waste management is one of the most important concepts of environmental management due to its direct effect on citizens’ life standards and public health. In this chapter it is aimed to compare underground waste bins and roadside waste bins in a solid waste collection system from economic perspective using type-2 fuzzy net present worth analysis . Results show that underground waste bins should be preferred instead of roadside bins even if they have higher initial investment costs.

Intelligence Systems in Environmental Management: Theory and Applications

This book offers a comprehensive reference guide to intelligence systems in environmental management. It provides readers with all the necessary tools for solving complex environmental problems, where classical techniques cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including ant colony, genetic algorithms, evolutionary algorithms, fuzzy multi-criteria decision making tools, particle swarm optimization, agent-based modelling, artificial neural networks, simulated annealing, Tabu search, fuzzy multi-objective optimization, fuzzy rules, support vector machines, fuzzy cognitive maps, cumulative belief degrees, and many others. To foster a better understanding, all the chapters include relevant numerical examples or casestudies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on complex environmental problems. Moreover, by extending all the main aspects of classical environmental solution techniques to its intelligent counterpart, the book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

New Product Selection Using Fuzzy Linear Programming and Fuzzy Monte Carlo Simulation

Investment decisions are important due to their critical role in organizations’ success. Sometimes, especially in uncertain conditions the results obtained from traditional analysis techniques can be different from the real world results. Due to this fact the techniques that take uncertainty into account are preferred in investment analysis to aware of the effect of an uncertain environment. In this paper, fuzzy Monte Carlo simulation method is used to determine the best investment strategy on new product selection for an organization in the condition when the fuzzy net present value is not the only point of concern for decision making.

Wind Energy Investment Analyses Based on Fuzzy Sets

Engineering economics deals with the investment decisions, where the investment parameters are very hard to estimate exactly. In the cases where we do not have the required data for parameter estimation, possibilistic approaches may be used. In this chapter, a brief literature review on wind energy investments is first presented. Later, the chapter gives present worth analysis (PWA) methods extended to fuzzy sets. The chapter introduces ordinary fuzzy PWA, type-2 fuzzy PWA, intuitionistic fuzzy PWA, and hesitant fuzzy PWA. A numerical application for each extension is presented.