Wind power is one of the most promising renewable energy sources that has gained enormous attention, especially in the electricity generation sector over the past decade in the United States. In this study Data Envelopment Analysis (DEA) is implemented to quantitatively evaluate the relative efficiencies of the 236 large utility-scale wind farms. Input- and output-oriented CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models are applied to pre-determined three input and three output variables. The sensitivity analysis is conducted for the robustness of DEA by introducing seven new models with the various combinations of input and output variables of the original model. Tobit regression models are developed for the second stage of the analysis to investigate the effects of specifications of the wind turbine technologies. DEA results indicate that two-thirds of the wind farms are operated efficiently. On average, 70% of the wind farms have a considerable potential for further improvement in operational productivity by expanding these wind farm projects, 24% of them should reduce their operational size to increase their productivity level, and 6% of them are operating wind power at the most productive scale size. Nonparametric statistical tests show that the most efficient wind farms are located in Oklahoma because of the relatively high wind speed resources. Tobit regression model indicates the selection of the brand of the wind turbine has a significant contribution to the productive efficiency of the wind farms. The results of this study shed some light on the current efficiency assessments of the 236 large utility-scale wind farms in the United States and the future of wind energy for both energy practitioners and policy makers.

The average global surface temperature increased by 0.85 °C since 1850 because of irrepressible increase of the concentration of greenhouse gases (GHG). Electricity generation is the primary source of GHG emissions in the United States. Hence, renewable energy sources, which produce a negligible amount of GHG emissions, have gained enormous attention, especially in the electricity generation sector over the past decade. Wind power is the second largest renewable energy source to generate electricity in the United States. Therefore, in this study, a two-stage Data Envelopment Analysis (DEA) is developed to quantitatively evaluate the relative efficiencies of the 39 state's wind power performances for the electricity generation. Both input- and output-oriented CCR (Charnes, Cooper, and Rhodes (1978)) and BCC (Banker, Charnes, and Cooper (1984)) models are applied to pre-determined four input and six output variables. The sensitivity analysis is conducted to test the robustness of the DEA models. Tobit regression models are conducted by using the DEA results for the second stage analysis. The DEA results indicate that more than half of the states operate wind power efficiently. Tobit regression indicates that early installed wind power was more expensive and less productive relative the currently installed wind power. Findings of this study shed some light on the current efficiency assessments of the states and the future of wind energy for both energy practitioners and policy makers.

Background: The COVID-19 pandemic forced many companies to transition to remote work, which has created a social distance among team members that may affect team performance. Although previous studies have examined the relationship of team cohesion and team performance, few have investigated the question whether remote work affects team performance. Specifically, this study examines the correlation between team cohesion and team performance by comparing the same teams working locally versus remotely. Objectives: The objective is to investigate the correlation between team cohesion and team performance based on whether teams work locally or remotely. Method: The study was quantitative, using regression analysis. Data was gathered at a software company in Sweden. Team cohesion was evaluated based on verbal mimicry via the Language Style Matching (LSM) algorithm, applied on chat messages. Team performance was evaluated based on git contributions and tickets done. Team efficiency was analyzed via Data Envelopment Analysis (DEA). Team efficiencies were analyzed in the context of both time periods, before and during the COVID-19 pandemic, and both work settings. Association between team efficiency and team cohesion was investigated based on the work setting. Tools such as Excel, R, Python, LIWC, and MS Forms were used. Analysis results: When efficiency is correlated with LSM score (cohesion) for teams working remotely, there is a significantly strong positive correlation, suggesting cohesion plays an important role on team efficiency when working remotely. This observation is in line with previous research on cohesion influence on performance of local teams. The change of work setting did not affect the cohesion level of teams. Conclusions: Teams working remotely can be as effective as teams working locally. Teams working remotely can be as cohesive as teams working locally. Cohesion is especially relevant for team performance when teams work remotely. Recommendations for future research: One suggestion is to add Social Network Analysis (SNA) in the study to enhance internal validity of team cohesion measurement. Additional research could be done by conducting a qualitative study to compare against the perceived cohesion and performance.

In a time of strained resources and dynamic environments, the importance of effective and efficient systems is critical. This dissertation was developed to address the need to use feedback from multiple stakeholder groups to define quality and assess an entity’s efficiency at achieving such quality. A decision support model with applicability to diverse domains was introduced to outline the approach. Three phases, (1) quality model development, (2) input-output selection and (3) relative efficiency assessment, captured the essence of the process which also delineates the approach per tool applied. This decision support model was adapted in higher education to assess academic departmental efficiency at achieving stakeholder-relative quality. Phase 1 was accomplished through a three round, Delphi-like study which involved user group refinement. Those results were compared to the criteria of an engineering accreditation body (ABET) to support the model’s validity to capture quality in the College of Engineering & Computer Science, its departments and programs. In Phase 2 the Analytic Hierarchy Process (AHP) was applied to the validated model to quantify the perspective of students, administrators, faculty and employers (SAFE). Using the composite preferences for the collective group (n=74), the model was limited to the top 7 attributes which accounted for about 55% of total preferences. Data corresponding to the resulting variables, referred to as key performance indicators, was collected using various information sources and infused in the data envelopment analysis (DEA) methodology (Phase 3). This process revealed both efficient and inefficient departments while offering transparency of opportunities to maximize quality outputs. Findings validate the potential of the ii Delphi-like, analytic hierarchical, data envelopment analysis approach for administrative decision-making in higher education. However, the availability of more meaningful metrics and data is required to adapt the model for decision making purposes. Several recommendations were included to improve the usability of the decision support model and future research opportunities were identified to extend the analyses inherent and apply the model to alternative areas.

No description available.

The implementation of lean manufacturing concepts has shown significant impacts on various industries. Numerous tools and techniques have been developed to tackle specific problems in order to eliminate wastes and carry out lean concepts. With the focus on "how to make a system leaner," little effort has been made on determining "how lean the system is." Lean assessment surveys evaluate the current status of a system qualitatively against predefined lean indicators. Lean metrics are developed to quantify performance of improvement initiatives, but each metric only focuses on one specific area. Value Stream Maps demonstrate the current and future states graphically with the emphasis on time-based performance only. A truly quantitative and synthesized measure for overall leanness has not been established. In some circumstances, being lean may not be the only goal for manufacturers. In order to compete in the rapidly changing marketplace, manufacturing systems should also be agile to respond quickly to uncertain demands. Nevertheless, being extremely agile may increase the cost of regular operations and reduce the leanness of the system. Similarly, being extremely lean may reduce flexibility and lower the agility level. Therefore, a manufacturing system should be agile enough to handle the uncertainty of demands and meanwhile be lean enough to deliver goods with competitive prices and lead time. In order to achieve the appropriate leanness level, a leanness measure is needed to address not only "how lean the system is" but also "how lean it should be." In this research, a methodology is proposed to quantitatively measure leanness level of manufacturing systems using the Data Envelopment Analysis (DEA) technique. The production process of each work piece is defined as a Decision Making Unit (DMU) that transforms inputs of Cost and Time into output Value. Using a Slacks-Based Measure (SBM) model, the DEA-Leanness Measure is developed to quantify the leanness level of each DMU by comparing the DMU against the frontier of leanness. A Cost-Time-Value analysis is developed to create virtual DMUs to push the frontier towards ideal leanness so that an effective benchmark can be established. The DEA-Leanness Measure provides a unit-invariant leanness score valued between 0 and 1, which is an indication of "how lean the system is" and also "how much leaner the system can be." With the help of Cost-Time Profiling technique, directions of potential improvement can be identified by comparing the profiles of DMUs with different leanness scores. The leanness measure can also be weighted between Cost, Time and Value variables. The weighted DEA-Leanness Measure provides a way to evaluate the impacts of improvement initiatives with an emphasis on the company's strategic focus. Performing the DEA-Leanness measurement requires detailed cost and time data. A Web-Based Kanban is developed to facilitate automated data collection and real-time performance analysis. In some circumstances where detailed data is not readily available but a Value Stream Maps (VSM) has been constructed, the applications of DEA-Leanness Measure based on existing VSM are explored. Besides pursuing leanness, satisfying a customer's demand pattern requires certain level of agility. Based on the DEA-Leanness Measure, appropriate leanness targets can be identified for manufacturing systems considering sufficient agility level. The Online-Delay and Offline-Delay Targets are determined to represent the minimum acceptable delays considering inevitable waste within and beyond a manufacturing system. Combining the two targets, a Lean-Agile Performance Index can then be derived to evaluate if the system has achieved an appropriate level of leanness with sufficient agility for meeting the customers' demand. Hypothetical cases mimicking real manufacturing systems are developed to verify the proposed methodologies. An Excel-based DEA-Leanness Solver and a Web-Kanban System have been developed to solve the mathematical models and to substantiate potential applications of the leanness measure in real world. Finally, future research directions are suggested to further enhance the results of this research. / Ph. D.

This research explores the feasibility of providing innovative and effective solutions for traffic congestion. The design of reservation systems is being considered as an alternative and/or complementary travel demand management (TDM) strategy. A reservation indicates that a user will follow a booking procedure defined by the reservation system before traveling so as to obtain the right to access a facility or resource. In this research, the reservation system is introduced for a cordon-based downtown road network, hereafter called the Downtown Space Reservation System (DSRS). The research is executed in three steps. In the first step, the DSRS is developed using classic optimization techniques in conjunction with an artificial intelligence technology. The development of this system is the foundation of the entire research, and the second and third steps build upon it. In the second step, traffic simulation models are executed so as to assess the impact of the DSRS on a hypothetical transportation road network. A simulation model provides various transportation measures and helps the decision maker analyze the system from a transportation perspective. In this step, multiple simulation runs (demand scenarios) are conducted and performance insights are generated. However, additional performance measurement and system design issues need to be addressed beyond the simulation paradigm. First, it is not the absolute representation of performance that matters, but the concept of relative performance that is important. Moreover, a simulation does not directly demonstrate how key performance measures interact with each other, which is critical when trying to understand a system structure. To address these issues, in the third step, a comprehensive performance measurement framework has been applied. An analytical technique for measuring the relative efficiency of organizational units, or in this case, demand scenarios called network Data Envelopment Analysis (DEA), is used. The network model combines the perspectives of the transportation service provider, the user and the community, who are the major stakeholders in the transportation system. This framework enables the decision maker to gain an in-depth appreciation of the system design and performance measurement issues. / Ph. D.

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 gestores / Pla Ferrando, ML. (2013). Modelos flexibles para la valoración de la eficiencia [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31521

Análisis de la eficiencia de la gestión hospitalaria en la Comunidad Valenciana. Influencia del modelo de gestión Introducción La salud es uno de los derechos humanos Fundamentales, que recoge la OMS en su Constitución de julio de (1946): • El goce del grado máximo de salud que se pueda lograr es uno de los derechos fundamentales de todo ser humano. • El derecho a la salud incluye el acceso a una atención sanitaria oportuna, aceptable, asequible y de calidad satisfactoria. • El derecho a la salud significa que los Estados deben crear las condiciones que permitan que todas las personas puedan vivir lo más saludablemente posible. La sanidad, constituye además, uno de los pilares fundamentales del estado de bienestar en los países desarrollados. La salud de los ciudadanos es un objetivo primordial de los estados, si bien requiere una especial atención y análisis desde un punto de vista económico que garantice su universalidad y su sostenibilidad. En el último decenio, hemos asistido al desarrollo de diferentes alternativas al modelo de gestión hospitalaria tradicional con el objetivo de asegurar una buena calidad en la atención sanitaria y optimizar la utilización de recursos públicos. Dentro de estas alternativas, uno de los modelos que ha despertado mayor interés en nuestro país, es el de la Concesión Administrativa. El primer hospital, bajo el modelo de Concesión Administrativa, comenzó a funcionar en Alzira, tras la aprobación por la Generalitat Valenciana de la Ley 15/1997 de 25 de abril, que habilita nuevas formas de gestión. Es por tanto un centro sanitario de la red pública, gestionado por una empresa privada, que presta atención sanitaria especializada a una población de referencia, a través de un acuerdo económico que establece un canon fijo por cada uno de los pacientes asignados, comprometiéndose además a realizar las inversiones necesarias para la infraestructura y equipamiento. Conjuntamente se establecen métodos de control, con compensación de pagos, tanto por los pacientes de referencia atendidos fuera de esta concesión, como por aquellos que pudieran atenderse sin pertenecer a ella. Este modelo se ha extendido posteriormente a un total de cinco departamentos de la Comunidad Valenciana y se ha implementado en otras comunidades autónomas bajo la denominación de Modelo Alzira. Objetivos El objetivo de esta tesis doctoral, es analizar la influencia del modelo gestión sanitaria (directa o concesión) que operan en la Comunidad Valenciana, en los aspectos relativos a la eficiencia económica y la calidad asistencial. Metodología Los datos necesarios para la realización de los análisis se han obtenido de las fuentes de información de la Conselleria de Sanidad de la Generalitat Valenciana para los hospitales públicos. Los datos económicos referentes a las concesiones administrativas (Alzira, Dénia, Torrevieja, Elche y Manises) han sido suministrados directamente por dichos centros. Para ambos casos, los datos hacen referencia a los años 2.009 y 2.010. Las variables incluidas en el análisis son: costes por proceso, indicadores de calidad, indicadores de actividad e indicadores estructurales. Para establecer comparativas en cuanto a la eficiencia y a los factores que influyen en ella, se han realizado análisis multivariantes y análisis no estocásticos Se realizó un análisis clúster jerárquico con el fin de agrupar y clasificar de forma homogénea los hospitales de la Comunidad Valenciana. El análisis envolvente de datos (DEA), ha sido utilizado para clasificar los hospitales en eficientes e ineficientes, en función de la gestión (directa o concesión), de forma global y por las unidades específicas para las áreas quirúrgica, hospitalaria, ambulatoria y de urgencias así como por cluster. Para ello se han combinado en los outputs indicadores de calidad y actividad. Resultados Los resultados obtenidos del estudio han sido: - El análisis de costes por paciente, ajustado por case mix, muestra que las concesiones presentan costes inferiores a la media en las áreas de cirugía y consultas externas. En el área de hospitalización, las concesiones están por encima del promedio. - En el Servicio de Urgencias, una de las concesiones analizada, está por encima del coste promedio. Estos resultados han sido significativos en el análisis de regresión. - Las concesiones obtuvieron mejores resultados en los indicadores de calidad analizados. - En el análisis DEA de la eficiencia global, dos de las tres concesiones alcanzaron el nivel de máxima eficiencia. De los hospitales de gestión directa nueve de los diecinueve hospitales públicos estudiados lo lograron. Conclusiones Los análisis del desempeño y de eficiencia muestran como el grupo de las concesiones obtiene resultados por encima de la media de los conseguidos por hospitales gestionados de forma directa, pero no siempre mejores. Los resultados de esta Tesis proporcionan una sólida base para para futuras líneas de investigación en el ámbito de la evaluación económica de la gestión sanitaria. No obstante es necesario que se realicen nuevos estudios con un mayor número de centros de colaboración público-privada. / Clemente Collado, AC. (2014). ANÁLISIS DE LA EFICIENCIA DE LA GESTIÓN HOSPITALARIA EN LA COMUNIDAD VALENCIANA. INFLUENCIA DEL MODELO DE GESTIÓN [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/44115

Yes / Over the past few decades, the banking sectors in Latin America have undergone rapid structural changes to improve the efficiency and resilience of their financial systems. The up-to-date literature shows that all the research studies conducted to analyze the above-mentioned efficiency are based on a deterministic data envelopment analysis (DEA) model or econometric frontier approach. Nevertheless, the deterministic DEA model suffers from a possible lack of statistical power, especially in a small sample. As such, the current research paper develops the technique of satisficing DEA to examine the still less explored case of Peru. We propose a Satisficing DEA model applied to 14 banks operating in Peru to evaluate the bank-level efficiency under a stochastic environment, which is free from any theoretical distributional assumption. The proposed model does not only report the bank efficiency, but also proposes a new framework for peer mining based on the Bayesian analysis and potential improvements with the bias-corrected and accelerated confidence interval. Our study is the first of its kind in the literature to perform a peer analysis based on a probabilistic approach.