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Amin, S. H., & Razmi, J. (2009). An integrated fuzzy model for supplier management: A case study of ISP selection and evaluation. Expert Systems with Applications, 36(4), 8639–8648.
Abstract: Supplier selection is a multi-criteria decision-making problem which consists of both qualitative and quantitative metrics. A lot of investigations have been published in the supplier selection area and it has been notified that in the majority of these publications supplier selection and evaluation and development have the same meaning. However, one needs integrated models to cover all of these stages. In addition, most of the proposed models focused on manufacturing environments and a few papers have been allocated for service industries. To our knowledge, no Internet service provider (ISP) selection and evaluation has been published up to now. In this paper, we propose a new framework on the basis of company’s strategy for supplier management including supplier selection, evaluation, and development. In the first phase, quality function deployment (QFD) is utilized to rank the best ISPs based on qualitative criteria. Then, a quantitative model is adopted to consider quantitative metrics. Finally, we compose two models and select the best ISPs. In the next phase, we propose a novel algorithm to evaluate selected ISPs from three perspectives: customer, performance, and competition. Meanwhile, the fuzzy logic and triangular fuzzy numbers are utilized to deal with vagueness of human thought. Furthermore, a case study is conducted to illustrate the stages of ISP selection and evaluation. The implementation of the proposed model is easy and do not need optimization background.
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Amiri, M., Zandieh, M., Vahdani, B., Soltani, R., & Roshanaei, V. (2010). An integrated eigenvector-DEA-TOPSIS methodology for portfolio risk evaluation in the FOREX spot market. Expert Systems with Applications, 37(1), 509–516.
Abstract: The foreign exchange market (FOREX) is the largest financial market in the world, with a volume of over $2 trillion daily. Decision making about buying and selling the existing products in this market depends on several effective factors which cause the high risk in it and make it a sensitive job. So in this paper a new method which is extracted from the multiple decision making methods named eigenvector-DEA-TOPSIS methodology is presented to evaluate the risk of the number of related portfolios to this market. The eigenvector technique is used to determine the weights of criteria and some linguistic terms are applied for assessing portfolio risks under each criterion, then in order to determine the value of linguistic terms we use the data envelopment analysis (DEA) method. Finally we use TOPSIS method for aggregating portfolio risks under different criteria into an overall risk score for each portfolio and ranking the portfolios according to their risks. The integrated eigenvector-DEA-TOPSIS methodology is applicable to any number of decision alternatives and is illustrated with a numerical example.
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Amirteimoori, A., & Tabar, M. M. (2010). Resource allocation and target setting in data envelopment analysis. Expert Systems with Applications, 37(4), 3036–3039.
Abstract: In this paper, we present a DEA-based method for allocating fixed resources or costs across a set of decision making units. In addition, we show how output targets can be set at the same time as decisions are made about allocating input resources. Numerical examples are presented to illustrate the application procedure of the proposed approach.
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Asosheh, A., Nalchigar, S., & Jamporazmey, M. (2010). Information technology project evaluation: An integrated data envelopment analysis and balanced scorecard approach. Expert Systems with Applications, 37(8), 5931–5938.
Abstract: Information technology (IT) is a tool crucial for enterprises to achieve a competitive advantage and organizational innovation. A critical aspect of IT management is the decision whereby the best set of IT projects is selected from many competing proposals. The optimal selection process is a significant strategic resource allocation decision that can engage an organization in substantial long-term commitments. However, making such decisions is difficult because there are lots of quantitative and qualitative factors to be considered in evaluation process. This paper has two main contributions. Firstly, it combines two well-established managerial methodologies, balanced scorecard (BSC) and data envelopment analysis (DEA), and proposes a new approach for IT project selection. This approach uses BSC as a comprehensive framework for defining IT projects evaluation criteria and uses DEA as a nonparametric technique for ranking IT projects. Secondly, this paper introduces a new integrated DEA model which identifies most efficient IT project by considering cardinal and ordinal data. Applicability of proposed approach is illustrated by using real world data of Iran Ministry of Science, Research and Technology.
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Azadeh, A., Saberi, M., & Anvari, M. (2010). An integrated artificial neural network algorithm for performance assessment and optimization of decision making units. Expert Systems with Applications, 37(8), 5688–5697.
Abstract: This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN algorithm is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). However, the proposed algorithm is capable of handling outliers and noise. This is shown by two examples related to outlier situations. It is also capable of performing optimization analysis and forecasting for a given set of data. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.
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Azadeh, A., Saberi, M., Anvari, M., & Izadbakhsh, H. R. (2009). A Meta heuristic approach for performance assessment of production units. Expert Systems with Applications, 36(3, Part 2), 6559–6569.
Abstract: There have been many efficiency frontier analysis methods reported in the literature. However, each of these methodologies has its strength as well as major limitations. This study proposes a Meta heuristic approach based on adaptive neural network (ANN) technique, fuzzy C-means and numerical taxonomy (NT) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. Homogenous test is done by NT. It is used to determine if the DMUs are homogenous or not. The proposed computational methods are able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. In this algorithm, for calculating the efficiency scores, a similar approach to za has been used. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale (under constant return to scale assumption). Also in non homogenous situation, for increasing DMUs’ homogeneousness, fuzzy C-means method is used to cluster DMUs. Two examples using real data are presented for illustrative purposes. Homogenous test result is positive in the first example, which deals with power generation sectors, and is negative in the second example dealing auto industries of various developed countries. Overall, we find that the proposed integrated algorithm based on ANN, fuzzy C-means and numerical taxonomy provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.
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Azadeh, A., Saberi, M., Gitiforouz, A., & Saberi, Z. (2009). A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation. Expert Systems with Applications, 36(8), 11108–11117.
Abstract: This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and complex estimation and forecasting. Computer simulation is developed to generate random variables for monthly electricity consumption. Various structures of ANFIS are examined and the preferred model is selected for estimation by the proposed algorithm. Finally, the preferred ANFIS and time series models are selected by Granger-Newbold test. Monthly electricity consumption in Iran from 1995 to 2005 is considered as the case of this study. The superiority of the proposed algorithm is shown by comparing its results with genetic algorithm (GA) and artificial neural network (ANN). This is the first study that uses a hybrid ANFIS computer simulation for improvement of electricity consumption estimation.
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Büyüközkan, G., & Öztürkcan, D. (2010). An integrated analytic approach for Six Sigma project selection. Expert Systems with Applications, 37(8), 5835–5847.
Abstract: Six Sigma is regarded as a well-structured methodology for improving the quality of processes and products. It helps achieve the company’s strategic goal through the effective use of project-driven approach. As Six Sigma is a project-driven methodology, it is essential to prioritize projects which provide maximum financial benefits to the organization. Generating and prioritizing the critical Six Sigma projects, however, are real challenges in practice. This study aims to develop a novel approach based on a combined ANP and DEMATEL technique to help companies determine critical Six Sigma projects and identify the priority of these projects especially in logistics companies. First of all the Six Sigma project evaluation dimension and components are determined. Decision Making Trial and Evaluation Laboratory (DEMATEL) approach is then applied to construct interrelations among criteria. The weights of criteria are obtained through analytic network process (ANP). An empirical case study from logistics industry is used to explore the effectiveness of the proposed approach.
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Çelebi, D., & Bayraktar, D. (2008). An integrated neural network and data envelopment analysis for supplier evaluation under incomplete information. Expert Systems with Applications, 35(4), 1698–1710.
Abstract: Supplier evaluation and selection are critical decision making processes that require consideration of a variety of attributes. Several studies have been performed for effective evaluation and selection of suppliers by utilizing several techniques such as linear weighting methods, mathematical programming models, statistical methods and AI based techniques. One of the successful evaluation methods proposed for this purpose is data envelopment analysis (DEA), that utilizes techniques of mathematical programming to evaluate the performance of a set of homogeneous decision making units, when multiple inputs and outputs need to be considered. It is often complicated, costly and sometimes impossible to acquire all necessary information from all potential suppliers to attain a reasonable set of similar input and output values which is an essential for DEA. The purpose of this study is to explore a novel integration of neural networks (NN) and data envelopment analysis for evaluation of suppliers under incomplete information of evaluation criteria.
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Celik, M., Cebi, S., Kahraman, C., & Er, I. D. (2009). Application of axiomatic design and TOPSIS methodologies under fuzzy environment for proposing competitive strategies on Turkish container ports in maritime transportation network. Expert Systems with Applications, 36(3, Part 1), 4541–4557.
Abstract: The strategic positions and geographical advantages of the Turkish container ports in the world transportation network create an excessive demand which seek urgent development strategies for managing ongoing problems in operational and administrative level. This paper proposes a hybrid approach on ensuring the competitiveness requirements for major Turkish container ports by utilizing fuzzy axiomatic design (FAD) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS) methodologies to manage strategic decision-making with incomplete information. The outcomes of the quantitative models are utilized as data input for SWOT analysis that provide additional contributions for identifying the development strategies on container ports. The proposed strategies on Turkish container ports can be originally recommended as guidelines both for port administrations and new enterprises in Turkish maritime industry.
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