8 research outputs found

    Data Envelopment Analysis Models and Software Packages for Academic Purposes

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    Data envelopment analysis (DEA) is a popular technique used in measuring the performance of an industry. DEA has wide applications in agriculture, manufacturing, health care, transportation, education, energy and environment, as well as banking and finance. However, many students and academicians are in dilemma in finding the appropriate software to execute a particular DEA model. In recent years, different DEA software packages have been developed by some universities and companies for both academic and commercial purposes. The software packages offered wide varieties of most recent DEA models that could be used in science and technology. Some of these software packages are free for academic users while others are commercialised. In this study, nine different DEA software packages were reviewed. Among them, only three are free for academic purposes while four have been commercialized. One of the former is still in the development stage, but expected to be available soon

    A proxy approach to dealing with the infeasibility problem in super-efficiency data envelopment analysis

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    Super-efficiency data envelopment analysis (SE-DEA) models are expressions of the traditional DEA models featuring the exclusion of the unit under evaluation from the reference set. The SE-DEA models have been applied in various cases such as sensitivity and stability analysis, measurement of productivity changes,outliers’ identification,and classification and ranking of decision making units (DMUs). A major deficiency in the SE-DEA models is their infeasibility in determining super-efficiency scores for some efficient DMUs when variable, non-increasing and non-decreasing returns to scale (VRS, NIRS, NDRS) prevail. The scope of this study is the development of an oriented proxy approach for SE-DEA models in order to tackle the infeasibility problem. The proxy introduced to the SE-DEA models replaces the original infeasible DMU in the sample and guarantees a feasible optimal solution. The proxy approach yields the same scores as the traditional SE-DEA models to the feasible DMUs

    A proxy approach to dealing with the infeasibility problem in super-efficiency data envelopment analysis

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    Super-efficiency data envelopment analysis (SE-DEA) models are expressions of the traditional DEA models featuring the exclusion of the unit under evaluation from the reference set. The SE-DEA models have been applied in various cases such as sensitivity and stability analysis, measurement of productivity changes,outliers’ identification,and classification and ranking of decision making units (DMUs). A major deficiency in the SE-DEA models is their infeasibility in determining super-efficiency scores for some efficient DMUs when variable, non-increasing and non-decreasing returns to scale (VRS, NIRS, NDRS) prevail. The scope of this study is the development of an oriented proxy approach for SE-DEA models in order to tackle the infeasibility problem. The proxy introduced to the SE-DEA models replaces the original infeasible DMU in the sample and guarantees a feasible optimal solution. The proxy approach yields the same scores as the traditional SE-DEA models to the feasible DMUs

    A Bayesian approach for correcting bias of data envelopment analysis estimators

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    The validity of data envelopment analysis (DEA) efficiency estimators depends on the robustness of the production frontier to measurement errors, specification errors and the dimension of the input-output space. It has been proven that DEA estimators, within the interval (0, 1], are overestimated when finite samples are used while asymptotically this bias reduces to zero. The non-parametric literature dealing with bias correction of efficiencies solely refers to estimators that do not exceed one. We prove that efficiency estimators, both lower and higher than one, are biased. A Bayesian DEA method is developed to correct bias of efficiency estimators. This is a two-stage procedure of super-efficiency DEA followed by a Bayesian approach relying on consistent efficiency estimators. This method is applicable to ‘small’ and ‘medium’ samples. The new Bayesian DEA method is applied to two data sets of 50 and 100 E.U. banks. The mean square error, root mean square error and mean absolute error of the new method reduce as the sample size increases

    A Bayesian approach for correcting bias of data envelopment analysis estimators

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    The validity of data envelopment analysis (DEA) efficiency estimators depends on the robustness of the production frontier to measurement errors, specification errors and the dimension of the input-output space. It has been proven that DEA estimators, within the interval (0, 1], are overestimated when finite samples are used while asymptotically this bias reduces to zero. The non-parametric literature dealing with bias correction of efficiencies solely refers to estimators that do not exceed one. We prove that efficiency estimators, both lower and higher than one, are biased. A Bayesian DEA method is developed to correct bias of efficiency estimators. This is a two-stage procedure of super-efficiency DEA followed by a Bayesian approach relying on consistent efficiency estimators. This method is applicable to ‘small’ and ‘medium’ samples. The new Bayesian DEA method is applied to two data sets of 50 and 100 E.U. banks. The mean square error, root mean square error and mean absolute error of the new method reduce as the sample size increases

    Contribution to the development of mathematical programming tools to assist decision-making in sustainability problems

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    L'activitat humana està excedint la capacitat de resposta de la Terra, el que pot tenir implicacions perjudicials per al futur benestar humà i del medi ambient. Sens dubte, severs canvis estructurals seran necessaris, el que exigeix prendre solucions eficaces davant els problemes emergents de sostenibilitat. En aquest context, aquesta tesi es centra en dues transformacions clau per re-connectar el desenvolupament humà amb el progrés sostenible: la "seguretat alimentària sostenible", desacoblant la intensificació agrícola de l'ús insostenible dels recursos; i el "model energètic sostenible", donant suport al canvi cap a una economia respectuosa amb el medi ambient. El marc metodològic consisteix a abordar diferents problemes mitjançant el desenvolupament d'eines sistemàtiques de programació matemàtica amb l'objectiu de donar suport a la presa de decisions i la formulació de polítiques conduents a la consecució del desenvolupament sostenible. Aquesta tesi doctoral inclou quatre contribucions principals en forma d'eines de decisió i suport de polítiques prou flexibles com per abordar diferents casos d'estudi. En primer lloc, es proposa una eina multiobjectiu per assignar àrees de cultiu considerant simultàniament criteris productius i mediambientals. En segon lloc, es proposa un model multiperíode per determinar plans de cultiu òptims i subsidis efectius per tal de promoure pràctiques agrícoles sostenibles. En tercer lloc, es proposa una metodologia per a analitzar la sostenibilitat que permet avaluar sistemes muticriteri i proporciona potencials millores d'acord amb els principis de la sostenibilitat. En quart lloc, es proposa un nou enfocament basat en l'optimització d'accions cooperatives amb l'objectiu de promoure i enfortir la cooperació internacional en la lluita contra el canvi climàtic La informació derivada de la investigació, com la presentada en aquesta tesi, pot tenir un paper fonamental en la transició cap a una nova era en la qual l'economia, la societat i el medi ambient coexisteixin com a pilars clau del desenvolupament sostenible.La actividades humanas están excediendo la capacidad de carga de la Tierra, lo que puede potencialmente generar implicaciones perjudiciales para el futuro bienestar humano y del medio ambiente. Sin duda son necesarios profundos cambios estructurales, lo que exige tomar soluciones eficaces ante los problemas emergentes de sostenibilidad. En este contexto, esta tesis se centra en dos transformaciones clave para reconectar el desarrollo humano con el progreso sostenible: la "seguridad alimentaria sostenible", desacoplando la intensificación agrícola del uso insostenible de los recursos; y el " modelo energético sostenible", apoyando el cambio hacia una economía respetuosa con el medio ambiente. El marco metodológico consiste en abordar distintos problemas mediante el desarrollo de herramientas sistemáticas de programación matemática cuyo objetivo es apoyar la toma de decisiones y la formulación de políticas tendentes hacia la consecución del desarrollo sostenible. La tesis incluye cuatro contribuciones principales en forma de herramientas de decisión y apoyo de políticas suficientemente flexibles para abordar diferentes casos de estudio. En primer lugar, se propone una herramienta multiobjetivo para asignar áreas de cultivo considerando simultáneamente criterios productivos y medioambientales. En segundo, se propone un modelo multiperiodo para determinar planes de cultivo óptimos y subsidios efectivos con el fin de promover prácticas agrícolas sostenibles. En tercero, se propone una metodología para realizar análisis de sostenibilidad que permite evaluar sistemas muticriterio y proporciona potenciales mejoras de acuerdo con principios de sostenibilidad. En cuarto lugar, se propone un nuevo enfoque basado en la optimización de acciones cooperativas con el objetivo de promover y fortalecer la cooperación internacional en la lucha contra el cambio climático La información derivada de la investigación, como la presentada en esta tesis, puede desempeñar un papel fundamental en la transición hacia una nueva era en la que la economía, la sociedad y el medio ambiente coexistan como pilares clave del desarrollo sostenible.Impacts from human activities are exceeding the Earth’s carrying capacity, which may lead to irreversible changes posing a serious threat to future human well-being and the environment. There is no doubt that an urgent shift is needed for sustainability, which calls for effective solutions when facing ongoing and emerging sustainability challenges. Against this background, this thesis focuses on two key structural transformations needed to reconnect the human development to sustained progress: the “food security transformation”, through decoupling the intensification of agricultural production from unsustainable use of resources; and the “clean energy transformation”, supporting the transition towards a more environmentally friendly economy. Methodologically, different sustainability issues are tackled by developing systematic mathematical programming tools aiming at supporting sustainable decision and policy-making which ultimately will lead to the development of more efficient mechanisms to foster a sustainable development. This thesis includes four major contributions in the form of decision and policy- support tools which are flexible and practical enough to address different case studies towards a more sustainable agriculture and energy future. First, a multi-objective tool is proposed which allows allocating cropping areas simultaneously maximizing the production and minimizing the environmental impact on ecosystems and resources. Second, a multi-period model is proposed which allows determining optimal cropping plans and effective subsidies to promote agricultural practices beneficial to the climate and the environment. Third, a novel methodology tailored to perform sustainability assessments is proposed which allows evaluating multi-criterion systems and providing improvements targets for such systems according to sustainability principles. Fourth, an optimised cooperative approach is proposed to promote and strengthen international cooperation in the fight against climate change. Research-based work as the one proposed herein may play a major role in the transition towards a new era where the economy, society and the environment coexist as key pillars of sustainable development

    Modelos flexibles para la valoración de la eficiencia

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    El objetivo en esta Memoría ha sido el análisis de eficiencia de un determinado sector empresarial, teniendo en cuenta dos problemas casi siempre presentes, y de naturaleza muy diferente, por una parte, que los datos que se manejan pueden ser imprecisos y, por tanto, afectar al resultado de cualquier estudio de eficiencia y, por otra parte, el deseo de ordenar las empresas (Unidades De Toma de Decisión) atendiendo a la medición de su eficiencia. Para la medición de la eficiencia se ha recurrido a la metodología no paramétrica del Análisis Envolvente de datos (DEA) aplicandola a empresas del sector textil muy cercanas a nosotros. Ahora bien, dado que consideramos que siempre existe alguna incertidumbre o un posible error en la medición de algunos datos (inputs y outputs), introducimos la limitación de la certeza con el tratamiento fuzzy de los datos, métodos que no requieren conocer ni aplicar hipótesis sobre distribuciones de probabilidad de esos datos, que dicho sea de paso, podría no ser fáctible bajo determinados supuestos de incertidumbre. Pero además de la medir la eficiencia pretendemos proporcionar más información que la mera separación dicotómica entre empresas eficientes o no eficientes. Para ello desarrollamos y aplicamos los modelos de super-efficiencyfuzzy y cross-efficiency-fuzzy, que nos permiten establecer una ordenación bajo incertidumbre. Con este trabajo hemos realizado un estudio amplio de la eficiencia bajo incertidumbre. Se observa que los resultados obtenidos aplicando los distintos métodos son similares. Además, estos métodos proporcionan más información sobre las unidades estudiadas que las que proporciona un solo índice de eficiencia. Estos métodos pueden ser aplicables a otros tipos de empresas, aportando nueva información que puede ayudar u orientar en la toma de decisiones de sus gestoresPla Ferrando, ML. (2013). Modelos flexibles para la valoración de la eficiencia [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31521TESI
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