Two decades was systematically reviewed on fuzzy MCDM techniques from 1994 to 2014.The database for review was 403 papers from more than 150 high-ranking journals.403 scholarly papers were grouped in four different main fields.Papers were classified based on utilizing, developing and proposing research papers. MCDM is considered as a complex decision-making tool involving both quantitative and qualitative factors. In recent years, several fuzzy FMCDM tools have been suggested to choosing the optimal probably options. The purpose of this paper is to review systematically the applications and methodologies of the fuzzy multi decision-making (FMCDM) techniques. This study reviewed a total of 403 papers published from 1994 to 2014 in more than 150 peer reviewed journals (extracted from online databases such as ScienceDirect, Springer, Emerald, Wiley, ProQuest, and Taylor & Francis). According to experts' opinions, these papers were grouped into four main fields: engineering, management and business, science, and technology. Furthermore, these papers were categorized based on authors, publication date, country of origin, methods, tools, and type of research (FMCDM utilizing research, FMCDM developing research, and FMCDM proposing research). The results of this study indicated that, in 2013, scholars have published papers more than other years. In addition, hybrid fuzzy MCDM in the integrated method and fuzzy AHP in the individual section were ranked as the first and second methods in use. Additionally, Taiwan was ranked as the first country that contributed to this survey, and engineering was ranked as the first field that has applied fuzzy DM tools and techniques.

Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.

A review of fuzzy AHP methods for decision-making with subjective judgements

In this paper geometrical representations for multicriteria decision problems are proposed. This new approach provides assistance to understand the conflictual aspects of the criteria and to tackle the problem of the weights associated to them. A generalized criterion, including a preference function, is first generated for each criterion. This allows to define unicriterion preference flows for which a geometrical representation can be obtained by using the Principal Components Analysis. The actions are represented by points and criteria by axes in the PCA plane. A decision axis taking into account the weights associated to the criteria can be defined. This technique provides the decision-maker with a considerable enrichment for the understanding of his problem: clusters of actions can be considered, the importance of the criteria can be evaluated, conflictual criteria are immediately detected, incomparability between actions is emphasized and explained, best compromise actions are easily selected, new decision-axes representing possible clusters of criteria can be considered, undesirable actions can be eliminated, … The technique consists in a powerful new qualitative decision tool. It is illustrated in the paper on some examples treated by the Promethee I and II methods. A didactic and user-friendly microcomputer code is available.

Geometrical representations for MCDA

An integrated fuzzy MCDM approach for supplier evaluation and selection

Supplier selection using ANP and ELECTRE II in interval 2-tuple linguistic environment

The quality and delivery performance of a typical company depends largely on the capability of its suppliers. It is therefore important for a company to evaluate the performance of its existing suppliers. However, the problem of supplier evaluation is rather complex when a group of decision makers responsible for these suppliers are geographically dispersed. Evaluating decisions requiring the elicitation of vague and imprecise linguistic information expressed in fuzzy terms. For this purpose, a hybrid fuzzy group decision-making approach for supplier evaluation is proposed, integrating the fuzzy analytic hierarchy process (F-AHP) and the fuzzy preference-ranking-organization-method-for-enrichment-of-evaluation (PROMETHEE) group decision support system (F-PROMETHEE GDSS) method. F-AHP is used to determine the weight of multiple criteria while F-PROMETHEE GDSS is applied for the final ranking of suppliers from a group decision viewpoint. The approach advances the supplier evaluation context in two aspects. First, it treats each decision maker uniquely in terms of the preference function and threshold levels. Second, it provides rapid communication between teams through video conferencing. The hybrid approach is used to evaluate the suppliers of a dairy company.

A fuzzy hybrid group decision support system approach for the supplier evaluation process

Problem statement: Knowledge and its management are considered as a foundation for creating competitive advantages in organizations. Most of large companies have allocated plenty of resources to Knowledge Management (KM) because they believe Knowledge and its management is a foundation for creating competitive advantages in organizations. However, implementing knowledge management projects in an organization requires essential organizational changes. The main purpose of this study was to explore KM success factors of Iranian research center to make a basis for evaluating the readiness of KM in them. Approach: In this study, success factors of knowledge management were extracted from literature review on papers represented between 1997 and 2009. Then the factors were categorized and effective and critical success factors in each group were determined. The results were validated and analyzed by a questionnaire through binomial test and approved by an expert panel. Results: The study revealed that KM success factors of Iranian research centers are: Knowledge strategy, management support, motivational encouragements to share knowledge, suitable technical infrastructure. It is obvious that continuous attention of management to these factors is vital for the success of knowledge management in organizations. Conclusion: Based on results, to improve current situation of KM in Iranian research center, KM and its benefits should be represented to managers to attract their support in organization. Then KM should be employed in strategic program of organization. Besides, implementing the projects of KM should be accompanied with reward and motivational systems to facilitate knowledge sharing and create proper organizational culture.

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Evaluating the Readiness of Iranian Research Centers in Knowledge Management

This paper describes the development of a fuzzy decision support system (FDSS) for the assessment of risk in E-commerce (EC) development. A Web-based prototype FDSS is suggested to assist EC project managers in identifying potential EC risk factors and the corresponding project risks. A risk analysis model for EC development using a fuzzy set approach is proposed and incorporated into the FDSS. For running the fuzzy set approach, the researchers use MATLAB software. The research methodology includes these stages: At first, identifying the factors effect on the development of the E-commerce with the help of experts; secondly, defining the suitable membership function for each factor. There are three layers in the fuzzy system that every layer's output is input for next layer. Then, the system calculates the final risk through SUGENO inference engine for E-commerce. Indeed the suggested architecture for the model with the help of experts is presented in this paper.

https://ijmbr.srbiau.ac.ir/article_16_d8a418eacc7990ddb623c51c65fc978e.pdf

Risk Analysis in E-commerce via Fuzzy Logic

A decision support system was researched and applied to a case study in the petrochemical industry. The participants were an insurance company underwriting the policies of oil and gas refineries located in a major oil producing nation. The Chemical Process Quantitative Risk Analysis methodology was applied as a framework to implement uncertainty quantification and risk analysis using a specialized commercial DSS software product. A gas vapor explosion was simulated at an oil refinery, to predict the fire and radiation damage. Costs and risks were entered into the model based on historical data. Loss estimates were generated for equipment and buildings located various distances (pressures) from the explosion origin. Overall, the DSS model predicted an expected loss of over $14,000,000 USD for equipment located in the 50 meter explosion radius, which represented a loss ratio of almost 52%. The losses predicted from the DSS model were comparable to the literature and to experiences of the case study company. The margin of error from the DSS model was less than ±5% which made it very reliable according to benchmarks.

Decision Support Model for Fire Insurance Risk Analysis in a Petrochemical Case Study

Although many studies have examined various types of single load demand prediction algorithms, it is yet a challenging decision to select the best predictor. Geographical characteristics are one of the most effective factors in load consumption patterns, the performance of the algorithms, and the accuracy of predictions. Therefore, developing a general framework in which meteorological characteristics determine the best predictor can be of importance. Meta-learning models, which are less considered in this area, propose a framework for algorithm selection by using a set of meta-features. In this paper, a meta-learning system is developed using exogenous weather variables as meta-features. The proposed system selects the best predictor among a pool of candidate forecasting algorithms including ARIMA, ARIMAX, MLR, NAR, NARX, and SVR. In statistical approaches, the volatility of the mean and variance of the load demand is modeled by GARCH process. The selection process uses a set of meta-features built on temperature and dew point. The empirical tests were performed on 30 samples of daily load demand taken from ten various geographical zones in Ontario, Canada. The achievements reveal that the characteristics of weather meta-features such as range of variation, skewness, and kurtosis play an important role in assigning the most suitable predictor to each sample set and both groups of statistical and artificial intelligence-based predictors were selected in different sample sets.

Reza Samizadeh

Papers

A fuzzy hybrid group decision support system approach for the supplier evaluation process

Evaluating the Readiness of Iranian Research Centers in Knowledge Management

Risk Analysis in E-commerce via Fuzzy Logic

Decision Support Model for Fire Insurance Risk Analysis in a Petrochemical Case Study

Meta-learning in multivariate load demand forecasting with exogenous meta-features