5- #1090 PLANEACION ESTRATÉGICA EN EL SERVICIO DE JUSTICIA USANDO ANÁLISIS ENVOLVENTE DE DATOS

La planeación estratégica tiene como objeto proveer el desempeño y evolución de los negocios en entornos competitivos, asociativos y cooperativos, tanto de labores públicas, privadas o de organizaciones no gubernamentales, ONGS, respecto al cumplimiento de sus propósitos (Pasupathy & Triantis, 2007). Esta acción puede ser útil para actividades presupuestales como de formulación, proposición, formulación y evaluación de políticas públicas (Medina, 2007).Este texto propone un procedimiento para apoyar el proceso de planeación estratégica basado en modelos de Análisis Envolvente de Datos (DEA) intertemporales multiplicativos (Lacko, Humy, & Razkosová, 2017), de forma tal que los resultados obtenidos permitan proponer acciones de mejora identificables para cada agrupación de unidad evaluada por enfoque basadas en el cambio de: los coeficientes de eficiencia (Khezrimotlagh, Zhu, Cook, & Toloo, 2019), los ponderadores virtuales de entradas y salidas, el cambio en los recursos y el cambio en las holguras (Khezrimotlagh, Zhu, Cook, & Toloo, 2019).De aquí se obtiene una identificación de los cambios tecnológicos, propuestas para la mejora en la prestación del servicio y elementos para el recorrido estratégico en la prestación del servicio de justicia (Harris II, 2000)

4- # 1089 SELECCIÓN DE VARIABLES Y FORMA FUNCIONAL USANDO ANÁLISIS ENVOLVENTE DE DATOS

Los modelos DEA, Data Envelopment Analysis, combinan diferentes tipos de variables para obtener una comparación relativa de la eficiencia de un objeto frente a otros (Cooper, Seiford, & Tone, 2000). Sin embargo, poco se dice acerca de los métodos y procedimientos para la selección de variables a ser incluidas o eliminadas de un modelo (Edirisingle & Zhang, 2010); tampoco se identifica la posibilidad de disponer de una forma funcional que vincule las variables de entrada y salida (Dyson, y otros, 2001).Este texto recopila y propone lineamientos para identificar la forma funcional que vincula a diferentes variables de entrada y salida (Khezrimotlagh, Zhu, Cook, & Toloo, 2019), su consistencia dimensional, así como un conjunto de criterios para la clasificación, comprensión, selección e inclusión de variables y su interpretación (Cakrr, 2017). Esto se toma sobre el modelo DEA Translogarítmico para la descripción de desempeño de un conjunto prestadores de servicios de Justicia en Cundinamarca, Colombia entre 2007 a 2016 (Lacko, Humy, & Razkosová, 2017)

A data-driven decision support framework for DEA target setting:an explainable AI approach

The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.</p

A data-driven decision support framework for DEA target setting:an explainable AI approach

The intention of target setting for Decision-Making Units (DMUs) in Data Envelopment Analysis (DEA) is to perform better than their peers or reach a reference efficiency level. However, most of the time, the logic behind the target setting is based on mathematical models, which are not achievable in practice. Besides, these models are based on decreasing/increasing inputs/outputs that might not be feasible based on DMU's potential in the real world. We propose a data-driven decision support framework to set actionable and feasible targets based on vital inputs-outputs for target setting. To do so, DMUs are classified in their corresponding Efficiency Frontier (EF) levels based on multiple EFs approach and a machine learning classifier. Then, the vital inputs-outputs are determined using an Explainable Artificial Intelligence (XAI) method. Finally, a Multi-Objective Counterfactual Explanation is developed based on DEA (MOCE-DEA) to lead DMU in reaching the reference EF by adjusting actionable and feasible inputs-outputs. We studied Iranian hospitals to evaluate the proposed framework and presented two cases to demonstrate its mechanism. The results show that the performance of the DMUs is improved to reach the reference EF for studied cases. Then, a validation was conducted with the primal DEA model to show the robust improvement of DMUs after adjusting their original value based on the generated solutions by the proposed framework. It demonstrates that the adjusted values can also improve DMUs' performance in the primal DEA model.</p

An Extended-Directional Mix-Efficiency Measure: Performance Evaluation of OECD Countries Considering NetZero

Conventional data envelopment analysis (DEA) models make the assumption of controllable inputs and desirable outputs. However, in many real-world applications, there are two major issues facing the management of decision-making units. The first one is how to deal with uncontrollable inputs whose levels are determined by exogenous fixed factors. The second is how to deal with undesirable outputs that are accompanied by desirable outputs. The effect of the operating environment is frequently captured by uncontrollable inputs and undesirable outputs. The modulation of these two factors into a directional DEA model is still in its infancy in the DEA literature. This paper proposes new directional mix-efficiency measure and slacks-based measure models. These two efficiency models are proposed in the context of uncontrollable inputs and undesirable outputs. The new metric looks at how well the input and/or output mix should change to achieve a fully efficient status by decreasing controllable inputs and undesirable outputs and/or increasing desirable outputs while keeping uncontrollable inputs constant. The new mix-efficiency measure is based on the directional distance function and the slacks-based measure. The usefulness and applicability of the proposed models are assessed by measuring the eco-efficiency of the Organization for Economic Co-Operation and Development (OECD) countries. The aim of the application is to measure efficiency in the context of NetZero, with a specific focus on reducing CO2 emissions. The findings reveal that six countries—France, Luxembourg, Germany, Norway, Sweden, and the UK—have achieved eco-efficiency; therefore, these countries function in the constant returns-to-scale (CRS) regio

OR for entrepreneurial ecosystems : a problem-oriented review and agenda

Innovation-driven entrepreneurship has become a focus for economic development and received increasing attention from policy makers and academics over the last decades. While consensus has been reached that context matters for innovation and entrepreneurship, little evidence and decision support exists for policy makers to effectively shape the environment for growth-oriented companies. We present the entrepreneurial ecosystem concept as a complex systems-based approach to the study of innovation-driven entrepreneurial economies. The concept, in combination with novel data sources, offers new opportunities for research and policy, but also comes with new challenges. The aim of this paper is to take stock of the literature and build bridges for more transdisciplinary research. First, we review emergent trends in ecosystem research and provide a typology of four overarching problems based on current limitations. These problems connect operational research scholars to the context and represent focal points for their contributions. Second, we review the operational research literature and provide an overview of how these problems have been addressed and outline opportunities for future research, both for the specific problems as well as cross-cutting themes. Operational research has been invaluable in supporting decision-makers facing complex problems in several fields. This paper provides a conceptual and methodological agenda to increase its contribution to the study and governance of entrepreneurial ecosystems

A lean construction and BIM interaction model for the construction industry

An Analytical Network Process (ANP) was created to test the Lean and BIM concepts with data collected from U.S. companies to find the success factors of the Lean/BIM framework. After an extensive literature review, a total of 17 sub-categories for Lean/BIM are classified into three clusters, namely Communication, Production, and Visualization. An ANP network is then established to station the links between the attributes of the framework while computing their importance weights. Eight experienced civil engineers took part in the questionnaire study to assess the relations between the attributes. The main purpose of this study is to reveal the synergy between Lean and BIM with different components reflecting this synergy and present the Lean and BIM synergy on a comprehensive model. The results indicate that Production is the prominent cluster and Production Control, Standardization and Information accuracy are the most important factors in the Lean/BIM synergy. To validate the model, five construction projects were selected to test and observe the results accordingly. The study is expected to help construction industry leaders set their priorities, benefit more from the interaction between Lean and BIM, and revise their strategies accordingly. This study identifies Lean/BIM categories and subcategories as a roadmap for research and implementation. In this context, the study reveals the relationship between the categories/subcategories along with the weights and most and less important categories for Lean/BIM implementation and research

Quantitative Methods for Economics and Finance

This book is a collection of papers for the Special Issue “Quantitative Methods for Economics and Finance” of the journal Mathematics. This Special Issue reflects on the latest developments in different fields of economics and finance where mathematics plays a significant role. The book gathers 19 papers on topics such as volatility clusters and volatility dynamic, forecasting, stocks, indexes, cryptocurrencies and commodities, trade agreements, the relationship between volume and price, trading strategies, efficiency, regression, utility models, fraud prediction, or intertemporal choice

Data envelopment analysis and big data

In the traditional data envelopment analysis (DEA) approach for a set of n Decision Making Units (DMUs), a standard DEA model is solved n times, one for each DMU. As the number of DMUs increases, the running-time to solve the standard model sharply rises. In this study, a new framework is proposed to significantly decrease the required DEA calculation time in comparison with the existing methodologies when a large set of DMUs (e.g., 20,000 DMUs or more) is present. The framework includes five steps: (i) selecting a subsample of DMUs using a proposed algorithm, (ii) finding the best-practice DMUs in the selected subsample, (iii) finding the exterior DMUs to the hull of the selected subsample, (iv) identifying the set of all efficient DMUs, and (v) measuring the performance scores of DMUs as those arising from the traditional DEA approach. The variable returns to scale technology is assumed and several simulation experiments are designed to estimate the running-time for applying the proposed method for big data. The obtained results in this study point out that the running-time is decreased up to 99.9% in comparison with the existing techniques. In addition, we illustrate the essential computation time for applying the proposed method as a function of the number of DMUs (cardinality), number of inputs and outputs (dimension), and the proportion of efficient DMUs (density). The methods are also compared on a real data set consisting of 30,099 electric power plants in the United States from 1996 to 2016.Web of Science27431054104