Agrell, P. J., & Steuer, R. E. (2000). ACADEA – a decision support system for faculty performance reviews. Journal of Multicriteria Decision Analysis, 9(5), 191–204.
Abstract: ACADEA, a multi-criteria decision support system for the performance review of individual faculty, is presented. Developed from the point of view of a department that is facing exogenously as well as self imposed objectives, the support system looks upon the aggregate performance of an academic department as the result of individual faculty member’s multi-criteria evaluations. Five objectives, research output, teaching output, external service, internal service and cost, are operationalized into criteria. The system is applied to a university department with 30 faculty members evaluated over a 3-year period. The results identify promotional candidates and reveal underlying problems in managerial consistency, departmental sub-groupings and the incentive structure. The outcomes of the support system are consistent with the position that equity in faculty governance does not necessarily imply equal loads on all tasks.
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Ahmad, N., Berg, D., & Simons, G. R. (2006). The Integration Of Analytical Hierarchy Process And Data Envelopment Analysis In A Multi-Criteria Decision-Making Problem. International Journal of Information Technology and Decision Making, 5(2), 263–276.
Abstract: This research focuses on developing a model that can be used to assess the performance of Small to Medium-Sized Manufacturing Enterprises (SMEs). The model will result from the integration of a decision tool called the Analytical Hierarchy Process (AHP) and a data analysis model called Data Envelopment Analysis (DEA). This research demonstrates that by eliminating flaws and taking advantage of each methodology’s specific characteristics in identifying and solving problems, the new integrated AHP/DEA model appears to be a logical and sensible solution in multi-criteria decision-making problem.
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Aleskerov, F., Ersel, H., & Yolalan, R. (2004). Multicriterial ranking approach for evaluating bank branch performance. International Journal of Information Technology and Decision Making, 3(2), 321–335.
Abstract: 14 ranking methods based on multiple criteria are suggested for evaluating the performance of the bank branches. The methods are explained via an illustrative example, and some of them are applied to a real-life data for 23 retail bank branches in a large-scale private Turkish commercial bank.
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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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André, F. J. (2009). Indirect elicitation of non-linear multi-attribute utility functions. A dual procedure combined with DEA. Omega, 37(4), 883–895.
Abstract: This paper proposes a non-interactive method to elicit non-linear multi-attribute utility functions which is based on duality results. The idea is to obtain a utility function which is compatible with the observed behavior of decision makers. The paper builds on a previous work by André and Riesgo [A non-interactive method to elicit non-linear multi-attribute utility functions. Theory and application to agricultural economics. European Journal of Operational Research 2008;181:793-807] but it eliminates an important shortcoming: the necessity to have an analytical expression of the efficient set or an estimation of it. The alternative approach presented in this paper consists in using a method of projection onto the true efficient set, based on data envelopment analysis (DEA). In a simulation exercise we check the ability of the method to recover true preference parameters in the presence of errors made by decision makers.
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André, F. J., Herrero, I., & Riesgo, L. (2010). A modified DEA model to estimate the importance of objectives with an application to agricultural economics. Omega, 38(5), 371–382.
Abstract: This paper demonstrates a connection between data envelopment analysis (DEA) and a non-interactive elicitation method to estimate the weights of objectives for decision-makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows us to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of multiple attribute decision analysis the basic contribution of this paper is a new interpretation in terms of efficiency of the non-interactive methodology employed to estimate weights in a multicriteria approach. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures that does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.
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Athanassopoulos, A. D., & Podinovski, V. V. (1997). Dominance and potential optimality in multiple criteria decision analysis with imprecise information. Journal of the Operational Research Society, 48(2), 142–150.
Abstract: This paper discusses multiple criteria models of decision analysis with finite sets of alternatives. A weighted sum of criteria is used to evaluate the performance of alternatives. Information about the weights is assumed to be in the form of arbitrary linear constraints. Conditions for checking dominance and potential optimality of decision alternatives are presented. In the case of testing potential optimality, the proposed appoach leads to the consideration of a couple of mutually dual linear programming problems. The analysis of these problems gives valuable information for the decision maker. In particular, if a decision alternative is not potentially optimal, then a mixed alternative dominating it is defined by a solution to one of the LP problems. This statement generalizes similar results known for some special cases. The interpretation of the mixed alternative is discussed and compared to its analogue in a data envelopment analysis context.
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Ballestero, E. (1999). Measuring efficiency by a single price system. European Journal of Operational Research, 115(3), 616–623.
Abstract: We propose a multi-criteria model to measure the relative levels of efficiency for a set of alternatives. Measurement is achieved by a single price system. This involves a marked difference between Data Envelopment Analysis (DEA) and the model herein. Neither the assumptions of the proposed model nor its mathematical solution are related to DEA. In contrast to DEA, the model allows for selecting a single efficiency optimum.
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Bayazit, O., Karpak, B., & Yagci, A. (2006). A Purchasing Decision: Selecting a Supplier for a Construction Company. Journal of Systems Science and Systems Engineering, 15(2), 217–231.
Abstract: Supplier selection is one of the most crucial activities performed by organizations because of its strategic importance. Supplier selection is a multi-objective problem involving both quantitative and qualitative criteria. Over the years a number of quantitative approaches have been tried. Although the Analytic Hierarchy Process (AHP) has previously been used in supplier selection problems, one major weakness of the application-oriented AHP literature is that it tends to focus on the mechanics of AHP instead of on the theoretical and practical implications associated with finding a solution. Though it is one of the most extensively used Multiple Criteria Decision Analysis methodologies, our literature search indicated that most studies found the best solution and stopped there, ignoring sensitivity analysis. Performing sensitivity analysis is very important for practical decision making, sometimes even as important as finding the best solution. In this paper for the first time a comprehensive application of AHP for a real-world case is presented along with sensitivity analysis in choosing the best suppliers for a Turkish construction company. As a result of this study the company decided to allocate the order quantities between the two top suppliers.
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Belton, V., & Vickers, S. P. (1993). Demystifying DEA-a visual interactive approach based on multiple criteria analysis. Journal of the Operational Research Society, 44(9), 883–896.
Abstract: Both Data Envelopment Analysis (DEA) and Multiple Criteria Analysis (MCA) can be used to assess the efficiency with which units perform similar tasks. This paper describes an approach derived from the integration of data envelopment analysis and a multi-attribute value function. This approach is implemented as a visual interactive decision support system, the use of which is illustrated by a practical application. The authors feel that this approach overcomes some of the limitations of the original DEA approach and, in particular, increases users’ understanding of DEA. The approach is particularly well suited to the analysis of the efficiency of a small number of units.
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