Cengiz Kahraman

MULTI-CRITERIA SUPPLIER SELECTION USING FUZZY AHP

A supplier selection decision inherently is a multi‐criterion problem. It is a decision of strategic importance to companies. The nature of this decision usually is complex and unstructured. Management science techniques might be helpful tools for these kinds of decision‐making problems. The aim of this paper is to use fuzzy analytic hierarchy process (AHP) to select the best supplier firm providing the most satisfaction for the criteria determined. The purchasing managers of a white good manufacturer established in Turkey were interviewed and the most important criteria taken into account by the managers while they were selecting their supplier firms were determined by a questionnaire. The fuzzy AHP was used to compare these supplier firms.

MULTI-ATTRIBUTE COMPARISON OF CATERING SERVICE COMPANIES USING FUZZY AHP: THE CASE OF TURKEY

Abstract Catering is the act of providing food and services or it may be defined as preparing or providing food for someone else to serve; or preparing, delivering and serving food at the premises of another person or event. The aim of this paper is to provide an analytical tool to select the best catering firm providing the most customer satisfaction. The customers of three Turkish catering firms were interviewed and the most important criteria taken into account by the customers while they were selecting their catering firms were determined by a designed questionnaire. The fuzzy analytic hierarchy process was used to compare these catering firms. The means of the triangular fuzzy numbers produced by the customers and experts for each comparison were successfully used in the pairwise comparison matrices.

A fuzzy optimization model for QFD planning process using analytic network approach

In both the quality improvement and the design of a product, the engineering characteristics affecting product performance are primarily identified and improved to optimize customer needs (CNs). Especially, the limited resources and increased market competition and product complexity require a customer-driven quality management and product development system achieving higher customer satisfaction. Quality function deployment (QFD) is used as a powerful tool for improving product design and quality, and procuring a customer-driven quality system. In this paper, an integrated framework based on fuzzy-QFD and a fuzzy optimization model is proposed to determine the product technical requirements (PTRs) to be considered in designing a product. The coefficients of the objective function are obtained from a fuzzy analytic network process (ANP) approach. Fuzzy analytic hierarchy process (AHP) is also used in the proposed framework. An application in a Turkish Company producing PVC window and door systems is presented to illustrate the proposed framework. � 2004 Elsevier B.V. All rights reserved.

Fuzzy group decision-making for facility location selection

The selection of a facility location among alternative locations is a multicriteria decision-making problem including both quantitative and qualitative criteria. The conventional approaches to facility location problem tend to be less effective in dealing with the imprecise or vagueness nature of the linguistic assessment. Under many situations, the values of the qualitative criteria are often imprecisely defined for the decision-makers. The aim of the paper is to solve facility location problems using different solution approaches of fuzzy multi-attribute group decision-making. The paper includes four different fuzzy multi-attribute group decision-making approaches. The first one is a fuzzy model of group decision proposed by Blin. The second is the fuzzy synthetic evaluation. The third is Yagers weighted goals method and the last one is fuzzy analytic hierarchy process. Although four approaches have the same objective of selecting the best facility location alternative, they come from different theoretic backgrounds and relate differently to the discipline of multi-attribute group decision-making. These approaches are extended to select the best facility location alternative by taking into account quantitative and qualitative criteria. A short comparative analysis among the approaches and a numeric example to each approach are given.

Fuzzy group decision making for selection among computer integrated manufacturing systems

A decision to invest in new manufacturing enabling technologies supporting computer integrated manufacturing (CIM) must include non-quantifiable, intangible benefits to the organization in meeting its strategic goals. Therefore, use of tactical level, purely economic, evaluation methods normally result in the rejection of strategically vital automation proposals. This paper includes four different fuzzy multi-attribute group decision-making methods. The first one is a fuzzy model of group decision proposed by Blin. The second is fuzzy synthetic evaluation, the third is Yagers weighted goals method, and the last one is fuzzy analytic hierarchy process. These methods are extended to select the best computer integrated manufacturing system by taking into account both intangible and tangible factors. A computer software for these approaches is developed and finally some numerical applications of these methods are given to compare the results of all methods.

Capital budgeting techniques using discounted fuzzy versus probabilistic cash flows

Risk analysis involves the development of the probability distribution for the measure of effectiveness. The risk associated with an investment alternative is generally either given as the possibility of an unfavorable value of the measure of effectiveness or measured by the variance of the measure of effectiveness. In an uncertain economic decision environment, an experts knowledge about discounting 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 paper, 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 given some numeric examples. 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.

A fuzzy multicriteria methodology for selection among energy alternatives

Since the correct energy policy affects economic development and environment, the most appropriate energy policy selection is excessively important. Recently some studies have concentrated on selecting the best energy policy and determining the best energy alternatives. In most of these studies, multicriteria and fuzzy approaches to energy policy making are frequently used. The fuzzy set theory is a powerful tool to treat the uncertainty in case of incomplete or vague information. In this paper, a fuzzy multicriteria decision-making methodology is suggested for the selection among energy policies. The methodology is based on the analytic hierarchy process (AHP) under fuzziness. It allows the evaluation scores from experts to be linguistic expressions, crisp or fuzzy numbers. In the application of the proposed methodology, the best energy policy is determined for Turkey.

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.

Fuzzy multicriteria disposal method and site selection for municipal solid waste.

The use of fuzzy multiple criteria analysis (MCA) in solid waste management has the advantage of rendering subjective and implicit decision making more objective and analytical, with its ability to accommodate both quantitative and qualitative data. In this paper a modified fuzzy TOPSIS methodology is proposed for the selection of appropriate disposal method and site for municipal solid waste (MSW). Our method is superior to existing methods since it has capability of representing vague qualitative data and presenting all possible results with different degrees of membership. In the first stage of the proposed methodology, a set of criteria of cost, reliability, feasibility, pollution and emission levels, waste and energy recovery is optimized to determine the best MSW disposal method. Landfilling, composting, conventional incineration, and refuse-derived fuel (RDF) combustion are the alternatives considered. The weights of the selection criteria are determined by fuzzy pairwise comparison matrices of Analytic Hierarchy Process (AHP). It is found that RDF combustion is the best disposal method alternative for Istanbul. In the second stage, the same methodology is used to determine the optimum RDF combustion plant location using adjacent land use, climate, road access and cost as the criteria. The results of this study illustrate the importance of the weights on the various factors in deciding the optimized location, with the best site located in Catalca. A sensitivity analysis is also conducted to monitor how sensitive our model is to changes in the various criteria weights.

A two phase multi-attribute decision-making approach for new product introduction

This study aims at improving the quality and effectiveness of decision-making in new product introduction. New product development has long been recognized as one of the corporate core functions to be competitive on an increasingly competitive global market. However, developing new products is a process involving risk and uncertainty. In order to solve this stochastic problem, companies need to evaluate their new product initiatives carefully and make accurate decisions. For this reason, a systematic decision process for selecting more rational new product ideas is proposed. Basically, two stages of decision-making are described: the identification of nondominated new product candidates and the selection of the best new product idea. These stages are composed of an integrated approach based on a fuzzy heuristic multi-attribute utility method and a hierarchical fuzzy TOPSIS method. Finally, an application is given to demonstrate the potential of the methodology.