34 research outputs found

    The role of multiplier bounds in fuzzy data envelopment analysis

    Get PDF
    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.The non-Archimedean epsilon ε is commonly considered as a lower bound for the dual input weights and output weights in multiplier data envelopment analysis (DEA) models. The amount of ε can be effectively used to differentiate between strongly and weakly efficient decision making units (DMUs). The problem of weak dominance particularly occurs when the reference set is fully or partially defined in terms of fuzzy numbers. In this paper, we propose a new four-step fuzzy DEA method to re-shape weakly efficient frontiers along with revisiting the efficiency score of DMUs in terms of perturbing the weakly efficient frontier. This approach eliminates the non-zero slacks in fuzzy DEA while keeping the strongly efficient frontiers unaltered. In comparing our proposed algorithm to an existing method in the recent literature we show three important flaws in their approach that our method addresses. Finally, we present a numerical example in banking with a combination of crisp and fuzzy data to illustrate the efficacy and advantages of the proposed approach

    Região de viabilidade e comparação de grupos para a performance DEA não arquimediana via regressão fracionada.

    Get PDF
    Considera-se a utilização da constante não arquimediana em modelos DEA-CCR. A aplicação de interesse é definida pela medida de performance dos centros de pesquisa da Empresa Brasileira de Pesquisa Agropecuária. Caracteriza-se uma região de viabilidade e sugere-se um valor para a constante. Tipos de DMUs são, então, comparados por meio do uso de regressão fracionada e do método da máxima quase-verossimilha nça. Conclui-se pela dominância dos centros de pesquisa do tipo produto. As medidas de performance clássica DEA-CCR e com restrições de folga não nula pela constante não arquimediana têm correlação de Spearman superior a 90%

    An extended multiple criteria data envelopment analysis model

    Get PDF
    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

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

    Get PDF
    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

    Using a decision-making process to evaluate efficiency and operating performance for listed semiconductor companies

    Get PDF
    Today’s high-tech industries face increasing competition and challenges. Thus, for hightech companies, making effective use of resources to enhance business performance and maintain the competitive advantage in the market has become increasingly important. Therefore, this study aimed to design a decision-making model for evaluating the efficiency and operating performance of Taiwan’s listed semiconductor companies in 2010 to provide a basis for improving business performance. In view of this, this study combines data envelopment analysis (DEA) and improved grey relational analysis (IGRA) as efficiency tools to measure relative efficiencies; the semiconductor companies are divided into two groups, efficient and inefficient. We then integrate the multiple criteria decision making (MCDM) method (e.g. VlseKriterijumska Optimizacija I Kompromisno Resenje, VIKOR), IGRA and the entropy weight method to evaluate the operating performance of the efficient and inefficient groups, respectively. Establishing a reasonable, objective and valid evaluation model to measure semiconductor companies’ operating efficiency can provide company managers, investors and policy makers with a reference for performance evaluation. First published online: 20 Jun 201

    Multi-scale modelling and optimisation of sustainable chemical processes

    Get PDF
    This dissertation explores the process modelling and optimisation of chemical processes under sustainability criteria. Resting on process systems engineering techniques combined with life cycle assessment (LCA), we present implementation strategies to improve flowsheet performance and reduce environmental impacts from early design stages. We first address the relevance of sustainability assessments in the sector and present process and environmental modelling techniques available. Under the observation that chemical processes are subject to market, technical, and environmental fluctuations, we next present an approach to account for these uncertainties. Process optimisation is then tackled by combining surrogate modelling, objective-reduction, and multi-criteria decision analysis tools. The framework proved the enhancement of the assessments by reducing the use of computational resources and allowing the ranking of optimal alternatives based on the concept of efficiency. We finally introduce a scheme to assess sustainable performance at a multi-scale level, from catalysis development to planet implications. This approach aims to provide insights about the role of catalysis and establish priorities for process development, while also introducing absolute sustainability metrics via the concept of ‘Planetary boundaries’. Ultimately, this allows a clear view of the impact that a process incurs in the current and future status of the Earth. The capabilities of the methods developed are tested in relevant applications that address challenges in the sector to attain sustainable performance. We present how concepts like circular economy, waste valorisation, and renewable raw materials can certainly bring benefits to the industry compared to their fossil-based alternatives. However, we also show that the development of new processes and technologies is very likely to shift environmental impacts from one category to another, concluding that cross-sectorial cooperation will become essential to meet sustainability targets, such as those determined by the Sustainable Development Goals.Open Acces

    Multiple-Criteria Decision Making

    Get PDF
    Decision-making on real-world problems, including individual process decisions, requires an appropriate and reliable decision support system. Fuzzy set theory, rough set theory, and neutrosophic set theory, which are MCDM techniques, are useful for modeling complex decision-making problems with imprecise, ambiguous, or vague data.This Special Issue, “Multiple Criteria Decision Making”, aims to incorporate recent developments in the area of the multi-criteria decision-making field. Topics include, but are not limited to:- MCDM optimization in engineering;- Environmental sustainability in engineering processes;- Multi-criteria production and logistics process planning;- New trends in multi-criteria evaluation of sustainable processes;- Multi-criteria decision making in strategic management based on sustainable criteria

    Advancing efficiency analysis using data envelopment analysis: the case of German health care and higher education sectors

    Get PDF
    The main goal of this dissertation is to investigate the advancement of efficiency analysis through DEA. This is practically followed by the case of German health care and higher education organizations. Towards achieving the goal, this dissertation is driven by the following research questions: 1.How the quality of the different DEA models can be evaluated? 2.How can hospitals’ efficiency be reliably measured in light of the pitfalls of DEA applications? 3.In measuring teaching hospital efficiency, what should be considered? 4.At the crossroads of internationalization, how can we analyze university efficiency? Both the higher education and the health care industries are characterized by similar missions, organizational structures, and resource requirements. There has been increasing pressure on universities and health care delivery systems around the world to improve their performance during the past decade. That is, to bring costs under control while ensuring high-quality services and better public accessibility. Achieving superior performance in higher education and health care is a challenging and intractable issue. Although many statistical methods have been used, DEA is increasingly used by researchers to find best practices and evaluate inefficiencies in productivity. By comparing DMU behavior to actual behavior, DEA produces best practices frontier rather than central tendencies, that is, the best attainable results in practice. The dissertation primarily focuses on the advancement of DEA models primarily for use in hospitals and universities. In Section 1 of this dissertation, the significance of hospital and university efficiency measurement, as well as the fundamentals of DEA models, are thoroughly described. The main research questions that drive this dissertation are then outlined after a brief review of the considerations that must be taken into account when employing DEA. Section 2 consists of a summary of the four contributions. Each contribution is presented in its entirety in the appendices. According to these contributions, Section 3 answers and critically discusses the research questions posed. Using the Translog production function, a sophisticated data generation process is developed in the first contribution based on a Monte Carlo simulation. Thus, we can generate a wide range of diverse scenarios that behave under VRS. Using the artificially generated DMUs, different DEA models are used to calculate the DEA efficiency scores. The quality of efficiency estimates derived from DEA models is measured based on five performance indicators, which are then aggregated into two benchmark-value and benchmark-rank indicators. Several hypothesis tests are also conducted to analyze the distributions of the efficiency scores of each scenario. In this way, it is possible to make a general statement regarding the parameters that negatively or positively affect the quality of DEA estimations. In comparison with the most commonly used BCC model, AR and SBM DEA models perform much better under VRS. All DEA applications will be affected by this finding. In fact, the relevance of these results for university and health care DEA applications is evident in the answers to research questions 2 and 4, where the importance of using sophisticated models is stressed. To be able to handle violations of the assumptions in DEA, we need some complementary approaches when units operate in different environments. By combining complementary modeling techniques, Contribution 2 aims to develop and evaluate a framework for analyzing hospital performance. Machin learning techniques are developed to perform cluster analysis, heterogeneity, and best practice analyses. A large dataset consisting of more than 1,100 hospitals in Germany illustrates the applicability of the integrated framework. In addition to predicting the best performance, the framework can be used to determine whether differences in relative efficiency scores are due to heterogeneity in inputs and outputs. In this contribution, an approach to enhancing the reliability of DEA performance analyses of hospital markets is presented as part of the answer to research question 2. In real-world situations, integer-valued amounts and flexible measures pose two principal challenges. The traditional DEA models do not address either challenge. Contribution 3 proposes an extended SBM DEA model that accommodates such data irregularities and complexity. Further, an alternative DEA model is presented that calculates efficiency by directly addressing slacks. The proposed models are further applied to 28 universities hospitals in Germany. The majority of inefficiencies can be attributed to “third-party funding income” received by university hospitals from research-granting agencies. In light of the fact that most research-granting organizations prefer to support university hospitals with the greatest impact, it seems reasonable to conclude that targeting research missions may enhance the efficiency of German university hospitals. This finding contributes to answering research question 3. University missions are heavily influenced by internationalization, but the efficacy of this strategy and its relationship to overall university efficiency are largely unknown. Contribution 4 fills this gap by implementing a three-stage mathematical method to explore university internationalization and university business models. The approach is based on SBM DEA methods and regression/correlation analyses and is designed to determine the relative internationalization and relative efficiency of German universities and analyze the influence of environmental factors on them. The key question 4 posed can now be answered. It has been found that German universities are relatively efficient at both levels of analysis, but there is no direct correlation between them. In addition, the results show that certain locational factors do not significantly affect the university’s efficiency. For policymakers, it is important to point out that efficiency modeling methodology is highly contested and in its infancy. DEA efficiency results are affected by many technical judgments for which there is little guidance on best practices. In many cases, these judgments have more to do with political than technical aspects (such as output choices). This suggests a need for a discussion between analysts and policymakers. In a nutshell, there is no doubt that DEA models can contribute to any health care or university mission. Despite the limitations we have discussed previously to ensure that they are used appropriately, these methods still offer powerful insights into organizational performance. Even though these techniques are widely popular, they are seldom used in real clinical (rather than academic) settings. The only purpose of analytical tools such as DEA is to inform rather than determine regulatory judgments. They, therefore, have to be an essential part of any competent regulator’s analytical arsenal

    Robust optimization in data envelopment analysis: extended theory and applications.

    Get PDF
    Performance evaluation of decision-making units (DMUs) via the data envelopment analysis (DEA) is confronted with multi-conflicting objectives, complex alternatives and significant uncertainties. Visualizing the risk of uncertainties in the data used in the evaluation process is crucial to understanding the need for cutting edge solution techniques to organizational decisions. A greater management concern is to have techniques and practical models that can evaluate their operations and make decisions that are not only optimal but also consistent with the changing environment. Motivated by the myriad need to mitigate the risk of uncertainties in performance evaluations, this thesis focuses on finding robust and flexible evaluation strategies to the ranking and classification of DMUs. It studies performance measurement with the DEA tool and addresses the uncertainties in data via the robust optimization technique. The thesis develops new models in robust data envelopment analysis with applications to management science, which are pursued in four research thrust. In the first thrust, a robust counterpart optimization with nonnegative decision variables is proposed which is then used to formulate new budget of uncertainty-based robust DEA models. The proposed model is shown to save the computational cost for robust optimization solutions to operations research problems involving only positive decision variables. The second research thrust studies the duality relations of models within the worst-case and best-case approach in the input \u2013 output orientation framework. A key contribution is the design of a classification scheme that utilizes the conservativeness and the risk preference of the decision maker. In the third thrust, a new robust DEA model based on ellipsoidal uncertainty sets is proposed which is further extended to the additive model and compared with imprecise additive models. The final thrust study the modelling techniques including goal programming, robust optimization and data envelopment to a transportation problem where the concern is on the efficiency of the transport network, uncertainties in the demand and supply of goods and a compromising solution to multiple conflicting objectives of the decision maker. Several numerical examples and real-world applications are made to explore and demonstrate the applicability of the developed models and their essence to management decisions. Applications such as the robust evaluation of banking efficiency in Europe and in particular Germany and Italy are made. Considering the proposed models and their applications, efficiency analysis explored in this research will correspond to the practical framework of industrial and organizational decision making and will further advance the course of robust management decisions
    corecore