Murat Gülbay

Fuzzy multi-criteria evaluation of industrial robotic systems

Industrial robots have been increasingly used by many manufacturing firms in different industries. While the number of robot manufacturers is also increasing with many alternative ranges of robots, potential end-users are faced with many options in both technical and economical factors in the evaluation of the industrial robotic systems. Industrial robotic system selection is a complex problem which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Hierarchical structure is a good approach to describe a complicated system. This paper proposes a fuzzy hierarchical TOPSIS model for the multi-criteria evaluation of the industrial robotic systems. An application is presented with some sensitivity analyses by changing the critical parameters.

Hierarchical fuzzy TOPSIS model for selection among logistics information technologies

Purpose – To develop a multi‐attribute decision making model for evaluating and selecting among logistic information technologies.Design/methodology/approach – First a multi‐attribute decision making model for logistic information technology evaluation and selection consisting of 4 main and 11 sub criteria is constructed, then a hierarchical fuzzy TOPSIS method is developed to solve the complex selection problem with vague and linguistic data. Sensitivity analysis is presented.Findings – Reviews the literature and provides a structured hierarchical model for logistic information technology evaluation and selection based on the premise that the logistic information technology evaluation and selection problem can be viewed as a product of tangible benefits, intangible benefits, policy issues and resources. Defines tangible benefits as cost savings, increased revenue, and return on investment; intangible benefits as customer satisfaction, quality of information, multiple uses of information, and setting tone...

α-Cut fuzzy control charts for linguistic data: α-Cut Fuzzy Control Charts

The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations that are ambiguous or not well defined. In the literature, there exist some fuzzy control charts developed for linguistic data that are mainly based on membership and probabilistic approaches. In this article, α‐cut control charts for attributes are developed. This approach provides the ability of determining the tightness of the inspection by selecting a suitable α‐level: The higher α the tighter inspection. The article also presents a numerical example and interprets and compares other results with the approaches developed previously.

Applications of Fuzzy Sets in Industrial Engineering: A Topical Classification

Summary: A rational approach toward decision-making should take into account human subjectivity, rather than employing only objective probability measures. This attitude towards the uncertainty of human behavior led to the study of a relatively new decision analysis field: Fuzzy decision-making. Fuzzy systems are suitable for uncertain or approximate reasoning, especially for the system with a mathematical model that is difficult to derive. Fuzzy logic allows decision-making with estimated values under incomplete or uncertain information. A major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy set theory has been used to model systems that are hard to define precisely. As a methodology, fuzzy set theory incorporates imprecision and subjectivity into the model formulation and solution process. Fuzzy set theory represents an attractive tool to aid research in industrial engineering (IE) when the dynamics of the decision environment limit the specification of model objectives, constraints and the precise measurement of model parameters. This chapter provides a survey of the applications of fuzzy set theory in IE.

Multi Attribute Performance Evaluation Using a Hierarchical Fuzzy TOPSIS Method

Summary. Performance of a faculty is vital both for students and school, and must be measured and evaluated for positive reinforcement to faculty. Faculty performance evaluation problem is a difficult and sensitive issue which has quantitative and qualitative aspects, complexity and imprecision. In literature many different approaches are proposed in order to evaluate faculty performance. To deal with imprecision and vagueness of evaluation measures, fuzzy multi-attribute evaluation techniques can be used. In this paper, a comprehensive hierarchical evaluation model with many main and sub-attributes is constructed and a new algorithm for fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) that enables taking into account the hierarchy in the evaluation model is proposed. The obtained results from this new fuzzy TOPSIS approach are compared with fuzzy Analytic Hierarchy Process (AHP) on an application in an engineering department of a university and some sensitivity analyses are presented.

Fuzzy multi-attribute cost–benefit analysis of e-services

E‐service evaluation is a complex problem in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. Cost–benefit analyses applied to various areas are usually based on the data under certainty or risk. In case of uncertain, vague, and/or linguistic data, the fuzzy set theory can be used to handle the analysis. In this article, after the evaluation attributes of e‐services and the fuzzy multi‐attribute decision‐making methods are introduced, a fuzzy hierarchical TOPSIS model is developed and applied to an e‐service provider selection problem with some sensitivity analyses. The developed model is a useful tool for the companies that prefer outsourcing for e‐activities. It is shown that service systems can be effectively evaluated by the proposed method.

FUZZY MULTI-CRITERIA EVALUATION OF INDUSTRIAL ROBOTIC SYSTEMS USING TOPSIS

Industrial robots have been increasingly used by many manufacturing firms in different industries. Although the number of robot manufacturers is also increasing with many alternative ranges of robots, potential end users are faced with many options in both technical and economical factors in the evaluation of the industrial robotic systems. Industrial robotic system selection is a complex problem, in which many qualitative attributes must be considered. These kinds of attributes make the evaluation process hard and vague. The hierarchical structure is a good approach to describing a complicated system. This chapter proposes a fuzzy hierarchical technique for order preference by similarity ideal solution (TOPSIS) model for the multi-criteria evaluation of the industrial robotic systems. An application is presented with some sensitivity analyses by changing the critical parameters.

Design of Fuzzy Process Control Charts for Linguistic and Imprecise Data

Summary. Even the first control chart was proposed during the 1920’s by Shewhart, today they are still subject to new application areas that deserve further attention. If the quality-related characteristics cannot be represented in numerical form, such as characteristics for appearance, softness, color, etc., then control charts for attributes are used. Except for the special cases, fuzzy control charts are used for attributes control charts such as p or c charts. The theory of classical control charts requires all the data to be exactly known. The major contribution of fuzzy set theory is its capability of representing vague data. Fuzzy logic offers a systematic base in dealing with situations, which are ambiguous or not well defined. Fuzzy control charts based on the fuzzy transformation methods are reviewed and a design for the control charts in the case of vague data using fuzzy sets as real valued interpretations of uncertainty and vagueness is proposed.

Applications of Fuzzy Capital Budgeting Techniques

Summary. In an uncertain economic decision environment, an experts knowledge about dicounting cash flows consists of a lot of vagueness instead of randomness. Cash amounts and interest rates are usually estimated by using educated guesses based on expected values or other statistical techniques to obtain them. Fuzzy numbers can capture the difficulties in estimating these parameters. In this chapter, the formulas for the analyses of fuzzy present value, fuzzy equivalent uniform annual value, fuzzy future value, fuzzy benefit-cost ratio, and fuzzy payback period are developed and some numeric examples are given. Then the examined cash flows are expanded to geometric and trigonometric cash flows and using these cash flows fuzzy present value, fuzzy future value, and fuzzy annual value formulas are developed for both discrete compounding and continuous compounding. Finally, a fuzzy versus stochastic investment analysis is examined by using the probability of a fuzzy event.