10,074 research outputs found

    Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework

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    Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework

    Canonical correlation analysis and DEA for azorean agriculture efficiency

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    In this paper we will document the application of canonical correlation analysis to variable aggregation using the correlations of the original variables with the canonical variates. A case study, about farms in Terceira Island, with a small data set is presented. In this data set of 30 farms we intend to use 17 input variables and 2 output variables to measure DEA efficiency. Without any data reduction procedure several problems known as “curse of dimensionality” are expected. With the data reduction procedures suggested it was possible to conclude quite acceptable and domain consistent conclusions.N/

    Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

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    In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

    Organic and Conventional Farming: a Comparison Analysis through the Italian FADN

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    This paper shows some results arisen from a wider research on economic and environmental sustainability of organic farming. It focuses on organic and conventional farming comparison through an investigation of Italian FADN data. In order to identify some of the main differences between organic and conventional farms a “distance analysis” has been carried out. The study aims to highlight some of the main characteristics of those two groups of farms to better address differences (if any) in production technology, costs and revenues. Furthermore it shows the findings of a non-parametric efficiency analysis on the Italian olive-growing farms. The purpose is to estimate difference in efficiency and productivity between organic and conventional olive producers. Results reveal that looking at the average values on Invested Areas, conventional farms’ Gross Production is significantly higher than the organic ones, as the Net Margin, as the Net Product and Costs. The average values on Total Labour Force instead, shown that, even if conventional farms still have higher values than organic ones, the “distance” become shorter. That means that the two groups are quite similar and that, even if organic farms still produce a lower “economic value”, they better compensate productive factors, especially in terms of Labour Force. Regards to efficiency analysis, we found that organic olive-growing farms are more able in using their disposable resources (with reference to their own frontier), and the higher efficiency permits them to compensate the lower productivity with respect to the conventional farms

    Operational Research in Education

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    Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions

    Azorean agriculture efficiency by PAR

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    The producers always aspire at increasing the efficiency of their production process. However, they do not always succeed in optimizing their production. In the last years, the interest on Data Envelopment Analysis (DEA) as a powerful tool for measuring efficiency has increased. This is due to the large amount of data sets collected to better understand the phenomena under study, and, at the same time, to the need of timely and inexpensive information. The “Productivity Analysis with R” (PAR) framework establishes a user-friendly data envelopment analysis environment with special emphasis on variable selection and aggregation, and summarization and interpretation of the results. The starting point is the following R packages: DEA (Diaz-Martinez and Fernandez-Menendez, 2008) and FEAR (Wilson, 2007). The DEA package performs some models of Data Envelopment Analysis presented in (Cooper et al., 2007). FEAR is a software package for computing nonparametric efficiency estimates and testing hypotheses in frontier models. FEAR implements the bootstrap methods described in (Simar and Wilson, 2000). PAR is a software framework using a portfolio of models for efficiency estimation and providing also results explanation functionality. PAR framework has been developed to distinguish between efficient and inefficient observations and to explicitly advise the producers about possibilities for production optimization. PER framework offers several R functions for a reasonable interpretation of the data analysis results and text presentation of the obtained information. The output of an efficiency study with PAR software is self- explanatory. We are applying PAR framework to estimate the efficiency of the agricultural system in Azores (Mendes et al., 2009). All Azorean farms will be clustered into homogeneous groups according to their efficiency measurements to define clusters of “good” practices and cluster of “less good” practices. This makes PAR appropriate to support public policies in agriculture sector in Azores.N/

    Data Envelopment Analysis (D.E.A.) for urban road system performance assessment

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    Improving the efficiency of transport networks by enhancing road system performance, lays the foundations for the positive change process within a city, achieving good accessibility to the area and optimizing vehicle flows, both in terms of cost, management and attenuation of environmental impacts. The performance of an urban road system can be defined according to different thematic areas such as traffic flow, accessibility, maintenance and safety, for which the scientific literature proposes different measurement indicators. However variations in performance are influenced by interventions which differ from one another, such as infrastructure, management, regulation or legislation, etc.. Therefore sometimes it is not easy to understand which areas to act on and what type of action to pursue to improve road network performance. Of particular interest are the tools based on the use of synthetic macro-indicators that are representative of the individual thematic areas and are able to describe the behavior of the entire network as a function of its characteristic elements. These instruments are of major significance when they assess performance not so much in absolute terms but in relative terms, i.e. in relation to other urban areas comparable to the one being examined. Therefore the objective of the proposed paper is to compare performances of different urban networks, using a non-parametric linear programming technique such as Data Envelopment Analysis (DEA), Farrel (1957), in order to provide technical support to the policy maker in the choice of actions to be implemented to make urban road systems efficient. This work is the conclusive study of road system performance analysis using DEA. The study forms part of a research project supported by grant. PRIN-2009 prot. 2009EP3S42_003, in which the University di Cagliari is a partner with a research team comprising the authors of this paper, and which addresses performance assessment of road networks, Fancello, Uccheddu and Fadda (2013a),(2013b)

    A Comparative Analysis of Organic and Conventional Farming trough the Italian FADN

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    This paper presents some results from a wider research on economic and environmental sustainability of organic farming. It aims to compare organic and conventional farming in order to identify some of the main differences between those groups of farms that participated in the official Farm Accountancy Data Network (FADN)-2003. The study is organized in two sections. The first part, after a brief literature review of the most recent statistical methodologies applied to identify the two similar groups of farms, presents some key economic variables (production, costs and revenues) and the most widely-used structural, economic and balance sheet indexes. The second part describes findings from a case study on the Italian fruit-growing sector. A non-parametric input-oriented frontier analysis (Data Envelopment Analysis, DEA)was used to evaluate which technique makes better use of their disposable productive inputs. Findings show that organic farmers can (partially) overcome the productivity gap (with respect to conventional ones) by more efficient use of their inputs (with respect to their own frontier)
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