Mehtap Dursun

A fuzzy MCDM approach for personnel selection

Many individual attributes considered for personnel selection such as organizing ability, creativity, personality, and leadership exhibit vagueness and imprecision. The fuzzy set theory appears as an essential tool to provide a decision framework that incorporates imprecise judgments inherent in the personnel selection process. In this paper, a fuzzy multi-criteria decision making (MCDM) algorithm using the principles of fusion of fuzzy information, 2-tuple linguistic representation model, and technique for order preference by similarity to ideal solution (TOPSIS) is developed. The proposed method is apt to manage information assessed using both linguistic and numerical scales in a decision making problem with multiple information sources. Furthermore, it enables managers to deal with heterogeneous information. The decision making framework presented in this paper employs ordered weighted averaging (OWA) operator that encompasses several operators as the aggregation operator since it can implement different aggregation rules by changing the order weights. The aggregation process is based on the unification of information by means of fuzzy sets on a basic linguistic term set (BLTS). Then, the unified information is transformed into linguistic 2-tuples in a way to rectify the problem of loss information of other fuzzy linguistic approaches. The computational procedure of the proposed framework is illustrated through a personnel selection problem reported in an earlier study.

An integrated fuzzy MCDM approach for supplier evaluation and selection

A methodology that uses QFD, fusion of fuzzy information, and 2-tuple linguistic representation is proposed.It manages non-homogeneous information in supplier selection with multiple information sources.It computes the weights of criteria and ratings of suppliers using interrelated HOQ matrices.It quantifies vagueness and pertinent relationships in supplier selection while tackling multi-granularity.The developed approach is illustrated through a case study. A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.

An integrated supplier selection methodology incorporating QFD and DEA with imprecise data

Supplier evaluation and selection is an important group decision making problem that involves not only quantitative criteria but also qualitative factors incorporating vagueness and imprecision. This paper proposes a novel fuzzy multi-criteria group decision making framework for supplier selection integrating quality function deployment (QFD) and data envelopment analysis (DEA). The proposed methodology allows for considering the impacts of inner dependence among supplier assessment criteria through constructing a house of quality (HOQ). The lower and upper bounds of the weights of supplier assessment criteria are identified by adopting fuzzy weighted average (FWA) method that enables the fusion of imprecise and subjective information expressed as linguistic variables. An imprecise DEA methodology is implemented for supplier selection, which employs the weights of supplier assessment criteria computed by FWA utilizing the data from the HOQ and the supplier ratings with respect to supplier assessment criteria. The application of the proposed framework is demonstrated through a case study in a private hospital in Istanbul.

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.

Taxonomy and review of non-deterministic analytical methods for supplier selection

Supplier selection is considered to be one of the most critical activities of purchasing management in a supply chain. Selecting the right suppliers significantly reduces the purchasing cost and improves corporate competitiveness. Diverse methods have been developed to date, which address the needs of the supplier selection process. This article presents a review of non-deterministic analytical methods reported in the literature for supporting the supplier selection decision. The review is based on an extensive search in the academic literature from 2001 to 2013. A taxonomy of the supplier selection methods is presented by classifying the published supplier selection studies into two major categories as stochastic methods and fuzzy methods. These methods are further divided into individual approaches and integrated approaches. The objective of this research is threefold. First, it classifies the existing supplier selection literature according to the methods employed and determines the most prevalently used approaches, and also highlights main advantages and shortcomings of these approaches. Second, recent trends in supplier evaluation and selection methodologies are examined. Finally, this study identifies the most widely used decision criteria for supplier selection.

An Integrated Approach Based on 2-Tuple Fuzzy Representation and QFD for Supplier Selection

With its need to trade-off multiple criteria exhibiting vagueness and imprecision, supplier selection is an important multi-criteria decision making problem. Vague and imprecise judgments inherent in numerous features of supplier selection justify the use of linguistic assessments rather than exact numerical values. In this chapter, a novel fuzzy multi-criteria group decision making approach integrating fusion of fuzzy information, 2-tuple linguistic representation model, and quality function deployment (QFD) is proposed for supplier selection. The developed fuzzy decision making approach employs ordered weighted averaging (OWA) operator and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set (BLTS). Afterwards, the collective performance values are transformed into linguistic 2-tuples to rectify the problem of loss of information encountered using other linguistic approaches. A supplier selection problem reported in an earlier study is used to illustrate the computational procedure of the proposed framework.

A 2-tuple integrated DEA-based approach for neuromarketing technology evaluation

Neuromarketing, which is an interdisciplinary area, concentrates on evaluating consumers’ cognitive and emotional reactions to different marketing stimuli. In spite of advantages, neuromarketing still requires development and lacks a strong theoretical framework. Techniques that are used in neuromarketing studies have different superiorities and limitations, and thus, there is a need for the evaluation of the relevance of these techniques. The purpose of this study is to introduce a novel integrated approach for the neuromarketing research area.,The proposed approach combines 2-tuple linguistic representation model and data envelopment analysis to obtain the most efficient neuromarketing technique. It is apt to handle information provided by using both linguistic and numerical scales with multiple information sources. Furthermore, it allows managers to deal with heterogeneous information, without loss of information.,The proposed approach indicates that functional magnetic resonance imaging (fMRI) is the best performing neuromarketing technology. Recently, fMRI has been widely used in neuromarketing research. In spite of its high cost, its main superiorities are improved spatial and temporal resolutions. On the other hand, transcranial magnetic stimulation (TMS) and positron emission tomography (PET) are ranked at the bottom because of their poor resolutions and lower willingness of participants.,This paper proposes a common weight data envelopment analysis (DEA)-based decision model to cope with heterogeneous information collected by the experts to determine the best performing neuromarketing technology. The decision procedure enables the decision-makers to handle the problems of loss of information and multi-granularity by using the fusion of 2-tuple linguistic representation model and fuzzy information. Moreover, a DEA-based common weight model does not require subjective experts’ opinions to weight the evaluation criteria.