14,326 research outputs found
Measuring Technical Efficiency of Dairy Farms with Imprecise Data: A Fuzzy Data Envelopment Analysis Approach
This article integrates fuzzy set theory in Data Envelopment Analysis (DEA) framework to compute technical efficiency scores when input and output data are imprecise. The underlying assumption in convectional DEA is that inputs and outputs data are measured with precision. However, production agriculture takes place in an uncertain environment and, in some situations, input and output data may be imprecise. We present an approach of measuring efficiency when data is known to lie within specified intervals and empirically illustrate this approach using a group of 34 dairy producers in Pennsylvania. Compared to the convectional DEA scores that are point estimates, the computed fuzzy efficiency scores allow the decision maker to trace the performance of a decision-making unit at different possibility levels.fuzzy set theory, Data Envelopment Analysis, membership function, α-cut level, technical efficiency, Farm Management, Production Economics, Productivity Analysis, Research Methods/ Statistical Methods, Risk and Uncertainty, D24, Q12, C02, C44, C61,
Affine arithmetic-based methodology for energy hub operation-scheduling in the presence of data uncertainty
In this study, the role of self-validated computing for solving the energy hub-scheduling problem in the presence of multiple and heterogeneous sources of data uncertainties is explored and a new solution paradigm based on affine arithmetic is conceptualised. The benefits deriving from the application of this methodology are analysed in details, and several numerical results are presented and discussed
Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework
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
Adverse effects of Interbank funds on bank efficiency: evidence from Turkish banking sector
This paper investigates the relationship between interbank funds and efficiencies is for the commercial banks operating in Turkey between 2001-2006. Data Envelopment Analysis (DEA) is executed to find the efficiency scores of the banks for each year, and fixed effects panel data regression is carried out, with the efficiency scores being the response variable. It is observed that interbank
funds (ratio) has negative effects on bank efficiency, while bank capitalization and loan ratio have positive, and profitability has insignificant effects. Our study serves as an illustrative evidence that interbank funds can have adverse effects in an emerging market
Land cover classification using fuzzy rules and aggregation of contextual information through evidence theory
Land cover classification using multispectral satellite image is a very
challenging task with numerous practical applications. We propose a multi-stage
classifier that involves fuzzy rule extraction from the training data and then
generation of a possibilistic label vector for each pixel using the fuzzy rule
base. To exploit the spatial correlation of land cover types we propose four
different information aggregation methods which use the possibilistic class
label of a pixel and those of its eight spatial neighbors for making the final
classification decision. Three of the aggregation methods use Dempster-Shafer
theory of evidence while the remaining one is modeled after the fuzzy k-NN
rule. The proposed methods are tested with two benchmark seven channel
satellite images and the results are found to be quite satisfactory. They are
also compared with a Markov random field (MRF) model-based contextual
classification method and found to perform consistently better.Comment: 14 pages, 2 figure
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs). The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler).Fuzzy Unification Tree, Dynamic Discrimination of Fuzzy Sets, DKMS, FRCOM
A methodology for the selection of new technologies in the aviation industry
The purpose of this report is to present a technology selection methodology to
quantify both tangible and intangible benefits of certain technology
alternatives within a fuzzy environment. Specifically, it describes an
application of the theory of fuzzy sets to hierarchical structural analysis and
economic evaluations for utilisation in the industry. The report proposes a
complete methodology to accurately select new technologies. A computer based
prototype model has been developed to handle the more complex fuzzy
calculations. Decision-makers are only required to express their opinions on
comparative importance of various factors in linguistic terms rather than exact
numerical values. These linguistic variable scales, such as âvery highâ, âhighâ,
âmediumâ, âlowâ and âvery lowâ, are then converted into fuzzy numbers, since it
becomes more meaningful to quantify a subjective measurement into a range rather
than in an exact value. By aggregating the hierarchy, the preferential weight of
each alternative technology is found, which is called fuzzy appropriate index.
The fuzzy appropriate indices of different technologies are then ranked and
preferential ranking orders of technologies are found. From the economic
evaluation perspective, a fuzzy cash flow analysis is employed. This deals
quantitatively with imprecision or uncertainties, as the cash flows are modelled
as triangular fuzzy numbers which represent âthe most likely possible valueâ,
âthe most pessimistic valueâ and âthe most optimistic valueâ. By using this
methodology, the ambiguities involved in the assessment data can be effectively
represented and processed to assure a more convincing and effective decision-
making process when selecting new technologies in which to invest. The prototype
model was validated with a case study within the aviation industry that ensured
it was properly configured to meet the
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