A bi-objective weighted model for improving the discrimination power in MCDEA

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordLack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. We found GPDEA models to be invalid and demonstrate that our proposed bi-objective multiple criteria DEA (BiO-MCDEA) outperforms the GPDEA models in the aspects of discrimination power and weight dispersion, as well as requiring less computational codes. An application of energy dependency among 25 European Union member countries is further used to describe the efficacy of our approach. © 2013 Elsevier B.V. All rights reserved.US

Improvement Of Discrimination Power And Weight Dispersion In Multi-Criteria Data Envelopment Analysis

Lack of discrimination power and poor weight dispersion remain major issues in Data Envelopment Analysis (DEA). Since the initial multiple criteria DEA (MCDEA) model developed in the late 1990s, only goal programming approaches; that is, the GPDEA-CCR and GPDEA-BCC were introduced for solving the said problems in a multi-objective framework. Kekurangan keupayaan mendiskriminasi dan kelemahan pengagihan pemberat kekal sebagai isu utama dalam Analisis Penyampulan Data (DEA). Semenjak model DEA berbilang kriteria (MCDEA) pertama yang dibentuk pada akhir tahun 1990an, hanya pendekatan pengaturcaraangol; yakni, GPDEA-CCR dan GPDEA-BCC telah diperkenalkan bagi menyelesaikan masalah berkenaan dalam konteks berbilang kriteria

An extended multiple criteria data envelopment analysis model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Several researchers have adapted the data envelopment analysis (DEA) models to deal with two inter-related problems: weak discriminating power and unrealistic weight distribution. The former problem arises as an application of DEA in the situations where decision-makers seek to reach a complete ranking of units, and the latter problem refers to the situations in which basic DEA model simply rates units 100% efficient on account of irrational input and/or output weights and insufficient number of degrees of freedom. Improving discrimination power and yielding more reasonable dispersion of input and output weights simultaneously remain a challenge for DEA and multiple criteria DEA (MCDEA) models. This paper puts emphasis on weight restrictions to boost discriminating power as well as to generate true weight dispersion of MCDEA when a priori information about the weights is not available. To this end, we modify a very recent MCDEA models in the literature by determining an optimum lower bound for input and output weights. The contribution of this paper is sevenfold: first, we show that a larger amount for the lower bound on weights often leads to improving discriminating power and reaching realistic weights in MCDEA models due to imposing more weight restrictions; second, the procedure for sensitivity analysis is designed to define stability for the weights of each evaluation criterion; third, we extend a weighted MCDEA model to three evaluation criteria based on the maximum lower bound for input and output weights; fourth, we develop a super-efficiency model for efficient units under the proposed MCDEA model in this paper; fifth, we extend an epsilon-based minsum BCC-DEA model to proceed our research objectives under variable returns to scale (VRS); sixth, we present a simulation study to statistically analyze weight dispersion and rankings between five different methods in terms of non-parametric tests; and seventh, we demonstrate the applicability of the proposed models with an application to European Union member countries

Penambahbaikan Sumber Jabatan Kecemasan menggunakan kaedah simulasi dan analisis pengumpulan data

Jabatan Kecemasan Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) menerima kedatangan pesakit yang ramai pada setiap hari menyebabkan jabatan ini kerap berdepan dengan masalah kesesakan. Justeru, objektif kajian ini adalah mengenal pasti model pengoptimuman terbaik untuk menambahbaik sumber bagi meningkatkan tahap kecekapan Jabatan Kecemasan PPUKM dan menyelesaikan masalah kesesakan jabatan. Kaedah simulasi digunakan bagi membina model jabatan kecemasan dengan pemboleh ubah yang digunakan dalam pemodelan simulasi ini adalah dikhususkan berdasarkan zon atau ruang rawatan. Alternatif penambahbaikan yang dicadangkan ini mengandungi konfigurasi bilangan sumber jabatan yang baru. Enam model gabungan yang digunakan terdiri daripada Model CCR dan Set Rujukan, Model BCC dan Set Rujukan, Model CCR dan Kecekapan-Super, Model BCC dan Kecekapan-Super, Model Bi-Objektif MCDEA-CCR dan Kecekapan Silang dan Model Bi-Objektif MCDEA-BCC dan Kecekapan Silang. Model Bi-Objektif MCDEA-BCC merupakan lanjutan kepada Model Bi-Objektif MCDEA-CCR daripada kajian terdahulu. Keputusan kajian menunjukkan Model Bi-Objektif MCDEA-BCC yang dibina telah memberikan bilangan alternatif penambahbaikan cekap yang paling kecil berbanding model-model gabungan lain. Melalui model gabungan ini juga satu alternatif penambahbaikan yang optimum yang telah dicadangkan dapat mengurangkan masa menunggu pesakit di Zon Hijau sebanyak 51% manakala peratusan penggunaan tenaga kerja sumber berjaya ditambahbaik agar lebih munasabah. Alternatif ini memerlukan susun atur kembali kedudukan sumber tanpa melakukan perubahan yang besar ke atas sistem asal

Penambahbaikan sumber jabatan kecemasan menggunakan kaedah simulasi dan analisis pengumpulan data = Resources improvement in emergency department using simulation and data envelopment analysis

Jabatan Kecemasan Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM) menerima kedatangan pesakit yang ramai pada setiap hari menyebabkan jabatan ini kerap berdepan dengan masalah kesesakan. Justeru, objektif kajian ini adalah mengenal pasti model pengoptimuman terbaik untuk menambahbaik sumber bagi meningkatkan tahap kecekapan Jabatan Kecemasan PPUKM dan menyelesaikan masalah kesesakan jabatan. Kaedah simulasi digunakan bagi membina model jabatan kecemasan dengan pemboleh ubah yang digunakan dalam pemodelan simulasi ini adalah dikhususkan berdasarkan zon atau ruang rawatan. Alternatif penambahbaikan yang dicadangkan ini mengandungi konfigurasi bilangan sumber jabatan yang baru. Enam model gabungan yang digunakan terdiri daripada Model CCR dan Set Rujukan, Model BCC dan Set Rujukan, Model CCR dan Kecekapan-Super, Model BCC dan Kecekapan-Super, Model Bi-Objektif MCDEACCR dan Kecekapan Silang dan Model Bi-Objektif MCDEA-BCC dan Kecekapan Silang. Model Bi-Objektif MCDEA-BCC merupakan lanjutan kepada Model Bi-Objektif MCDEA-CCR daripada kajian terdahulu. Keputusan kajian menunjukkan Model Bi-Objektif MCDEA-BCC yang dibina telah memberikan bilangan alternatif penambahbaikan cekap yang paling kecil berbanding model-model gabungan lain. Melalui model gabungan ini juga satu alternatif penambahbaikan yang optimum yang telah dicadangkan dapat mengurangkan masa menunggu pesakit di Zon Hijau sebanyak 51% manakala peratusan penggunaan tenaga kerja sumber berjaya ditambahbaik agar lebih munasabah. Alternatif ini memerlukan susun atur kembali kedudukan sumber tanpa melakukan perubahan yang besar ke atas sistem asa

Métodos e modelos de discriminação na metodologia DEA

Mestrado em Contabilidade e Gestão das Instituições FinanceirasNeste trabalho procura-se apresentar um estudo sobre a metodologia Data Envelopment Analysis (DEA), mais precisamente sobre os métodos/modelos de discriminação. Numa primeira fase, começa-se por fazer uma referência à avaliação de desempenho organizacional e à sua evolução, identificar os métodos tradicionais de avaliação e as suas limitações. Posteriormente, referem-se os modelos clássicos da metodologia DEA, o modelo CCR (Charnes, Cooper & Rhodes, 1978) e o modelo BCC (Banker, Charnes & Cooper, 1984). Porém, a aplicabilidade destes modelos apresenta algumas limitações que condicionam a utilização desta metodologia, nomeadamente a distribuição irrealista dos pesos e a falta de poder discriminativo entre as unidades eficientes. De forma a minimizar estas limitações vários métodos/modelos de discriminação têm surgido, designadamente o método das restrições sobre os pesos, o método de ajuste dos níveis de input/output para captação de juízos de valor, o método das restrições aos inputs/outputs virtuais e, por fim, os modelos multiobjectivo, alguns dos quais serão apresentados neste trabalho. No sentido de demonstrar a relevância que a aplicação desta metodologia pode ter numa organização, o modelo CCR e alguns dos métodos/modelos de discriminação são aplicados a vinte instituições bancárias, que operavam em Portugal no ano de 2014, de forma a avaliar a eficiência de cada instituição. Serão apresentados os resultados obtidos desta aplicação prática, bem como uma análise a esses resultados.This work aims to present a study on the methodology Data Envelopment Analysis (DEA), more precisely the methods/models of discrimination. In a initial it begins to make reference to the evaluation of organizational performance and to their development, identify the traditional methods of evaluation and its limitations. Later it refers to the classical models of DEA methodology, the CCR model (Charnes, Cooper & Rhodes, 1978) and BCC model (Banker, Charnes & Cooper, 1984). However, the applicability of these models present some limitations which affect the use of this methodology, particularly the unrealistic weight distribution and the lack of discriminatory power between efficient units. In order to mitigate these limitations various methods/models of discrimination have emerged, namely the method of restrictions about the weights, the method of adjusting levels of input/output for capture of value judgments, the method of the inputs/outputs virtual constraints and, finally, multi-objective models, some of which will be presented in this work. In order to demonstrate the relevance to the application of this methodology in an organization, the CCR model and some of the methods/models of discrimination are applied to twenty banks, operating in Portugal in the year 2014, in order to assess the efficiency of each institution. The results will be presented in this practical application, as well as an analysis of these results.N/

A bi-level multi-objective data envelopment analysis model for estimating profit and operational efficiency of bank branches

Data Envelopment Analysis (DEA) is a powerful method for analyzing the performance of decision making units (DMUs). Traditionally, DEA is applied for estimating the performance of a set of DMUs through measuring a single perspective of efficiency. However, in recent years, due to increasing competition in various industries, modern enterprises focus on enhancing their performance by measuring efficiencies in different aspects, separately or simultaneously. This paper proposes a bi-level multi-objective DEA (BLMO DEA) model which is able to assess the performance of DMUs in two different hierarchical dimensions, simultaneously. In the proposed model, we define two level efficiency scores for each DMU. The aim is to maximize these two efficiencies, simultaneously, for each DMU. Since the objective functions at both levels are fractional, a fuzzy fractional goal programming (FGP) methodology is used to solve the proposed BLMO DEA model. The capability of the proposed model is illustrated by a numerical example. Finally, to practically validate the proposed model, a real case study from 45 bank’s branches is applied. The results show that the proposed model can provide a more comprehensive measure for efficiency of each bank’s branch based on simultaneous measuring of two different efficiencies, profit and operational efficiencies, and by considering the level of their importance

Metodologia DEA e optimização multiobjectivo

Mestrado em Contabilidade e Gestão das Instituições FinanceirasA presente dissertação destina-se a apresentar um estudo sobre métodos de avaliação do desempenho organizacional, mais precisamente sobre a metodologia DEA (Data Envelopment Analysis) que permite considerar vários aspectos no processo de avaliação para além do aspecto financeiro. Nesse estudo começa-se por apresentar os modelos clássicos da DEA, o modelo CCR (Charnes, Cooper e Rhodes (1978)) e o modelo BCC (Banker, Charnes e Cooper (1984)). A aplicação prática destes modelos revela um fraco poder de discriminação de unidades eficientes e uma distribuição irrealista dos pesos que contribuem para o cálculo da eficiência. Para contornar estes problemas, vários métodos têm sido propostos. Um desses métodos consiste na utilização da programação multiobjectivo e é objeto de estudo neste trabalho. Alguns dos modelos apresentados foram utilizados para determinar a eficiência de 27 instituições bancárias que operaram em Portugal durante o ano de 2012. Esta aplicação real permitiu avaliar quantitativamente as diferenças entre o modelos usados, nomeadamente entre o modelo CCR e o modelo de programação multiobjectivo proposto por Ghasemi, Ignatius and Emrouznejad (2014), que é uma versão multiobjectivo do modelo CCR. Os resultados obtidos evidenciam uma considerável capacidade deste último modelo para contornar os problemas de aplicação do modelo CCR.This thesis aims to present a study on evaluation methods of organizational performance, specifically on the DEA methodology (Data envelopment Analysis) which allows to consider several aspects in the evaluation process beyond the financial aspect. This study begins by presenting the classical models of DEA, CCR model (Charnes, Cooper and Rhodes (1978)) and the BCC model (Banker, Charnes and Cooper (1984)). The implementation of this models show a weak discrimination of power efficient units and unrealistic weight distribution which are used to calculate the efficiency. To circumvent these problems, several methods have been proposed. One of these methods is the use of multiobjective programming that is the object of study in this work. Some of the presented models were used to determine the efficiency of 27 banks that operated in Portugal during 2012. This application allowed us to assess quantitatively the differences between the used models, in particular between the CCR model and the model proposed by Ghasemi multiobjective programming, Ignatius and Emrouznejad (2014), which is a multiobjective version of the CCR model. The results show a considerable capacity of this last model to circumvent the problems of applying the CCR model

Carbon efficiency evaluation:an analytical framework using fuzzy DEA

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers

A fuzzy expected value approach under generalized data envelopment analysis

Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models - fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers