A fuzzy DEA model with undesirable fuzzy outputs and its application to the banking sector in India

Data envelopment analysis (DEA) is a widely used technique for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and multiple outputs. However, in real life applications, undesirable outputs may be present in the production process which needs to be minimized. The present study endeavors to propose a DEA model with undesirable outputs and further to extend it in fuzzy environment in view of the fact that input/output data are not always available in exact form in real life problems. We propose a fuzzy DEA model with undesirable fuzzy outputs which can be solved as crisp linear program for each α in (0, 1] using α-cut approach. Further, cross-efficiency technique is applied to increase the discrimination power of the proposed models and to rank the efficient DMUs at every α in (0, 1]. Moreover, for better understanding of the proposed methodology, we present a numerical illustration followed by an application to the banking sector in India. This is the first study which attempts to measure the performance of public sector banks (PuSBs) in India using fuzzy input/output data for the period 2009–2011. The results obtained from the proposed methodology not only depict the impact of undesirable output on the performance of PuSBs but also analyze efficiently the influence of the presence of uncertainty in the data over the efficiency results. The findings show that the efficiency results of many PuSBs vary with the variation in α during the selected period.     

A fuzzy DEA and knapsack formulation integrated model for project selection

Project selection has become crucial in the fields of science and engineering. This paper discusses the specific problem of selecting a portfolio of projects that achieves an organization's objectives without exceeding limited capital resources, especially when each project possesses vague input and output data in the selection. In this paper, a data envelopment analysis (DEA), knapsack formulation and fuzzy set theory integrated model is proposed to deal with the problem, and the model is demonstrated via a case study problem in engineering-procurement-construction (EPC) industry. Moreover, this paper applies three constraint handling techniques, which are factor-free penalty function based, to transform a constrained optimization problem into an unconstrained problem, and for the first time adopts the artificial bee colony (ABC) algorithm to search the solutions. The performances of these three constraint handling techniques with respect to the ABC algorithm are compared for the first time in this paper.     

A fuzzy multi-objective two-stage DEA model for evaluating the performance of US bank holding companies

This paper investigates the association between the performance of bank holding companies (BHCs) and their intellectual capital (IC). We start from constructing an innovation ratio two-stage DEA model and then applies fuzzy multiple objective programming approaches to calculate the efficiency score. This model provides a common scale for comparing performance, increases the discriminating power, and simplifies the calculation process. The links between IC and the BHCs’ performance are also investigated by means of the truncated-regression model, and a positive relationship between them is found. The decision-making matrix combined with an efficiency improvement map proposed in this study can clearly define the benchmark that can be emulated by inefficient BHCs and help BHC managers to develop appropriate strategies needed to enhance their overall efficiency.     

A new fuzzy DEA model for evaluation of efficiency and effectiveness of suppliers in sustainable supply chain management context

Sustainable supply chain management (SSCM) has received much consideration from corporate and academic over the past decade. Sustainable supplier performance evaluation and selection plays a significant role in establishing an effective SSCM. One of the techniques that can be used for sustainable supplier performance evaluation and selection is data envelopment analysis (DEA). In real world problems, the inputs and outputs might be imprecise. This paper develops an integrated DEA enhanced Russell measure (ERM) model in fuzzy context to select the best sustainable suppliers. A case study is presented to exhibit the efficacy of the proposed method for sustainable supplier selection problem in a resin production company. The case study demonstrates that the proposed model can measure effectiveness, efficiency, and productivity in uncertain environment with different α levels. Also, it shows that the proposed model aids decision makers to deal with economic, social, and environmental factors when selecting sustainable suppliers.     

A fuzzy group data envelopment analysis model for high-technology project selection: A case study at NASA

The assessment and selection of high-technology projects is a difficult decision making process at the National Aeronautic and Space Administration (NASA). This difficulty is due to the multiple and often conflicting objectives in addition to the inherent technical complexities and valuation uncertainties involved in the assessment process. As such, a systematic and transparent decision making process is needed to guide the assessment process, shape the decision outcomes and enable confident choices to be made. Various methods have been proposed to assess and select high-technology projects. However, applying these methods has become increasingly difficult in the space industry because there are many emerging risks implying that decisions are subject to significant uncertainty. The source of uncertainty can be vagueness or ambiguity. While vague data are uncertain because they lack detail or precision, ambiguous data are uncertain because they are subject to multiple interpretations. We propose a data envelopment analysis (DEA) model with ambiguity and vagueness. The vagueness of the objective functions is modeled by means of multi-objective fuzzy linear programming. The ambiguity of the input and output data is modeled with fuzzy sets and a new α-cut based method. The proposed models are linear, independent of α-cut variables, and capable of maximizing the satisfaction level of the fuzzy objectives and efficiency scores, simultaneously. Moreover, these models are capable of generating a common set of multipliers for all projects in a single run. A case study involving high-technology project selection at NASA is used to demonstrate the applicability of the proposed models and the efficacy of the procedures and algorithms.     

Fuzzy rough DEA model: A possibility and expected value approaches

Uncertainty is certain in the world of uncertainty. Measuring the performance of any entity in such an uncertain environment is unavoidable. Fuzzy rough data envelopment analysis (FRDEA) provides a room to evaluate the relative efficiency of homogenous entities, widely known as decision making units (DMUs) in the data envelopment analysis (DEA) literature. This paper attempts to create a fuzzy rough DEA model by integrating the classical DEA, fuzzy set theory, and rough set theory, which apparently provide a way to accommodate the uncertainty. Moreover, in contrast to the probability approach, this paper provides a pavement to measure the relative efficiency of any given DMUs in line with the possibility approach along with the fuzzy rough expected value operator.     

A new network epsilon-based DEA model for supply chain performance evaluation

Supply chain performance evaluation problems are inherently complex problems with multilayered internal linking activities and multiple entities. Data Envelopment Analysis (DEA) has been used to evaluate the relative performance of organizational units called Decision Making Units (DMUs). However, the conventional DEA models cannot take into consideration the complex nature of supply chains with internal linking activities. Network DEA models using radial measures of efficiency are used for supply chain performance evaluation problems. However, these models are not suitable for problems where radial and non-radial inputs and outputs must be considered simultaneously. DEA models using Epsilon-Based Measures (EBMs) of efficiency are proposed for a simultaneous consideration of radial and non-radial inputs and outputs. We extend the EBM model and propose a new Network EBM (NEBM) model. The proposed NEBM model combines the radial and non-radial measures of efficiency into a unified framework for solving network DEA problems. A case study is presented to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a supply chain performance evaluation problem in the semiconductor industry.     

A comparison of three methods for principal component analysis of fuzzy interval data

Vertices Principal Component Analysis (V-PCA), and Centers Principal Component Analysis (C-PCA) generalize Principal Component Analysis (PCA) in order to summarize interval valued data. Neural Network Principal Component Analysis (NN-PCA) represents an extension of PCA for fuzzy interval data. However, also the first two methods can be used for analyzing fuzzy interval data, but they then ignore the spread information. In the literature, the V-PCA method is usually considered computationally cumbersome because it requires the transformation of the interval valued data matrix into a single valued data matrix the number of rows of which depends exponentially on the number of variables and linearly on the number of observation units. However, it has been shown that this problem can be overcome by considering the cross-products matrix which is easy to compute. A review of C-PCA and V-PCA (which hence also includes the computational short-cut to V-PCA) and NN-PCA is provided. Furthermore, a comparison is given of the three methods by means of a simulation study and by an application to an empirical data set. In the simulation study, fuzzy interval data are generated according to various models, and it is reported in which conditions each method performs best.     

Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran

Development of a fuzzy inference model is a complex multi-step process in which we encounter a large number of parameters such as type and number of membership functions, fuzzy operators, defuzzification and implication methods and etc. There is currently very little literature on the topic of the best selection of parameters for development of expert based inference models. In this study we developed a fuzzy rule based model, which uses available farm management data as required inputs, for the environmental assessment of farming systems. We also tried to make an analysis on the efficiency of current mathematical parameters in the development of our fuzzy model. Finally, in a practical example we demonstrate the applicability of the developed model for improvement of environmental status of the cane farming in Iran.A Mamdani fuzzy inference model with two inference engines was developed to combine five basic input indexes, which were selected as indicators of farms environmental status based on the experts' interview and scientific knowledge. To validate the developed model, we inserted several cycles of analysis using graphical and global sensitivity methods on the model and compared the model outcomes with experts' viewpoints. Using these analysis methods, we also evaluated the effects of changes in operators, membership function shape and defuzzification methods, on the model outcomes and their sensitivities.In this study, fuzzy inference emerged as a suitable, uncomplicated and effective tool for development of environmental assessment models. Totally, performance of one parameter was highly influenced by other parameters. For the selection of one parameter its interaction with other parameters had to be considered. Type, shape and the number of membership functions were from the most effective parameters for development of the model and significantly influenced the other factors. Case study results showed that environmental indexes of sugarcane production can enhance between 37 and 59% using simple improving strategies.     

A novel hybrid MCDM model based on fuzzy DEMATEL,fuzzy ANP and fuzzy VIKOR for city logistics concept selection

City logistics (CL) tends to increase efficiency and mitigate the negative effects of logistics processes and activities and at the same time to support the sustainable development of urban areas. Accordingly, various measures and initiatives are applying and various conceptual solutions are defining. The effects vary depending on the characteristics of the city. This paper proposes a framework for the selection of the CL concept which would be most appropriate for different participants, stakeholders, and which would comply with attributes of the surroundings. CL participants have different, usually conflicting goals and interests, so it is necessary to define a large number of criteria for concepts evaluation. On the other hand, the importance of the criteria is dependent on the specific situation, i.e., a large number of factors describing the surroundings. In situations like this, selecting the best alternative is a complex multi-criteria decision-making (MCDM) problem consisting of conflicting and uncertain elements. A novel hybrid MCDM model that combines fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), fuzzy Analytical Network Process (ANP) and fuzzy Višekriterijumska Optimizacija i kompromisno Rešenje (VIKOR) methods is developed in this paper. The model provides support to decision makers (planners, city administration, logistics service providers, users, etc.) when selecting the CL concept, which is successfully performed in this paper for the City of Belgrade.     

A combination of hidden Markov model and fuzzy model for stock market forecasting

This paper presents a novel combination of the hidden Markov model (HMM) and the fuzzy models for forecasting stock market data. In a previous study we used an HMM to identify similar data patterns from the historical data and then used a weighted average to generate a ‘one-day-ahead’ forecast. This paper uses a similar approach to identify data patterns by using the HMM and then uses fuzzy logic to obtain a forecast value. The HMM's log-likelihood for each of the input data vectors is used to partition the dataspace. Each of the divided dataspaces is then used to generate a fuzzy rule. The fuzzy model developed from this approach is tested on stock market data drawn from different sectors. Experimental results clearly show an improved forecasting accuracy compared to other forecasting models such as, ARIMA, artificial neural network (ANN) and another HMM-based forecasting model.     

DEA models with a constant input for SRI mutual funds with an application to European and Swedish funds

Data envelopment analysis (DEA) allows one to take into account the degree of social responsibility of mutual funds, together with financial risk and return. This contribution proposes some DEA models in which the input and output variables are focused on the main determinants of investments in socially responsible investing (SRI) mutual funds. Unlike other DEA models, a constant initial capital and the final value of the investment are considered; this ensures the positivity of all variables, even during financial crises. The initial capital deposited by an investor is assumed to be equal for all funds, so that we have a constant input. The implications of the presence of a constant input in DEA models are studied, which have important consequences for the analysis of the performance of mutual funds, in particular with regard to the type of returns to scale. The models proposed are applied to the European data to evaluate the performance of SRI mutual funds in the period June 2006 to June 2009. Moreover, a specific analysis compares the performance of SRI and non‐SRI mutual funds, in order to determine if SRIs require a sacrifice in terms of financial rewards. Finally, a more detailed investigation is carried out for the Swedish SRI mutual funds.     

A model for dimension reduction in pattern recognition using continuous data

An approach to transform continuous data to finite dimensional data is briefly outlined. A model to reduce the dimension of the finite dimensional data is developed for the case when the covariance matrices are not necessarily equal. Necessary and sufficient conditions with respect to the spatial properties of the means and covariance matrices are given so that the linear transformation of data of higher dimensions to lower dimensions does not increase the probabilities of misclassification.     

A fuzzy group quality function deployment model for e-CRM framework assessment in agile manufacturing

The rapid growth of the Internet and the expansion of electronic commerce applications in manufacturing have given rise to electronic customer relationship management (e-CRM) which enhances the overall customer satisfaction. However, when confronted by the range of e-CRM methods, manufacturing companies struggle to identify the one most appropriate to their needs. This paper presents a novel structured approach to evaluate and select the best agile e-CRM framework in a rapidly changing manufacturing environment. The e-CRM frameworks are evaluated with respect to their customer and financial oriented features to achieve manufacturing agility. Initially, the e-CRM frameworks are prioritized according to their financial oriented characteristics using a fuzzy group real options analysis (ROA) model. Next, the e-CRM frameworks are ranked according to their customer oriented characteristics using a hybrid fuzzy group permutation and a four-phase fuzzy quality function deployment (QFD) model with respect to three main perspectives of agile manufacturing (i.e., strategic, operational and functional agilities). Finally, the best agile e-CRM framework is selected using a technique for order preference by similarity to the ideal solution (TOPSIS) model. We also present a case study to demonstrate the applicability of the proposed approach and exhibit the efficacy of the procedures and algorithms.     

A hybrid model based on differential fuzzy logic relationships and imperialist competitive algorithm for stock market forecasting

In this study, a new kind of fuzzy set in fuzzy time series’ field is introduced. It works as a trend estimator to be appropriate for fuzzy time series forecasting by reconnoitering trend of data appropriately. First, the historical data are fuzzified into differential fuzzy sets, and then differential fuzzy relationships are calculated. Second, differential fuzzy logic groups are established by grouping differential fuzzy relationships. Finally, in the defuzzification step, the forecasts are calculated. However, for increasing the accuracy of the models, an evolutionary algorithm, namely imperialist competitive algorithm is injected, to train the model. A massive stock data from four main stock databases have been selected for model validation. The final project, has shown that outperformed its counterparts in term of accuracy.     

A DEA benchmarking methodology for project planning and management of new product development under decentralized profit-center business model

In today’s highly competitive marketplace, technology-driven organizations widely adopt decentralized profit-center business model. In order to complete a series of new product development (NPD) activities on time and within budgetary constraints, the NPD managers need an objective benchmarking approach to gain accurate perception on the relations of resource allocations, profits, costs and times for each NPD activity. Thus, this study employs the data envelopment analysis (DEA) concept to put forward a benchmarking planning and management methodology to optimize the NPD activities within a profit center for achieving the goal of maximal profit and satisfying the resource constraints. By applying the real case of the electric motor scooter NPD project, this research demonstrates the method’s real case application with superior results, comparing to other existing approaches.     

A multivariate model of fuzzy integrated logical forecasting method (M-FILF) and multiplicative time series clustering: A model of time-varying volatility for dry cargo freight market

The aim of this paper is to improve the fuzzy logical forecasting model (FILF) by utilizing multivariate inference and the partitioning problem for an exponentially distributed time series by using a multiplicative clustering approach. Fuzzy time series (FTS) is a growing study field in computer science and its superiority is indicated frequently. Since the conventional time series analysis requires various pre-conditions, the FTS framework is very useful and convenient for many problems in business practice. This paper particularly investigates pricing problems in the shipping business and price-volatility relationship is the theoretical point of the proposed approach. Both FTS and conventional time series results are comparatively presented in the final section and superiority of the proposed method is explicitly noted.     

Improvement of risk assessment in the FMEA using nonlinear model,revised fuzzy TOPSIS,and support vector machine

In every organization, performing accurate risk assessment along with consideration of increasing accidents is a necessary tool to prevent and reduce the fatal and non-fatal consequences of their occurrence. One of the most popular methods of risk assessment is Failure Mode and Effects Analysis, which evaluate failure modes in a system by using risk priority numbers (RPNs). These methods have been criticized for including several deficiencies such as the effect of personal ’opinions, the same importance of the factors and risk rating. The present work utilizes a hybrid approach based on support vector machine and fuzzy inference system to decrease the effect of personal's opinions in determining the factors of the severity and occurrence. Also, Logarithmic Fuzzy Preference Programming is used to determine the crisp weight of dependent factor of FMEA and revised fuzzy TOPSIS used for more accurate ranking of risks. One main feature of the proposed model is that it can be used to evaluate safety risks in all organizations. To investigate the suitability of this approach, the proposed model was presented in the Copper leaching factory, Kerman, Iran. The results showed that this model has the ability to predict severity and occurrence refers to occupational accidents which occurred in a 5-year period (2012–2017) with accuracy of 87% and 95%, respectively. Also, based on the results, it was found that the weights of severity, occurrence, and detection were 0.479, 0.335, and 0.186, respectively. Results of the ranking process showed that the risk of fall from height and stucking between the objects had the highest and the lowest priority, respectively.