1,767 research outputs found
Measuring efficiency of a hierarchical organization with fuzzy DEA method
The paper analyses how the data envelopment analysis (DEA) and fuzzy set theory can be used to measure and evaluate the efficiency of a hierarchical system with n decision making units and a coordinating unit. It is presented a model for determining the of activity levels of decision making units so as to achieve both fuzzy objectives of achieving global target levels of coordination unit on the inputs and outputs and individual target levels of decision making units, and then some methods to resolve fuzzy models are proposed.fuzzy DEA, policy making in multi-level organisations, efficiency analysis
Contextualized property market models vs. Generalized mass appraisals: An innovative approach
The present research takes into account the current and widespread need for rational valuation methodologies, able to correctly interpret the available market data. An innovative automated valuation model has been simultaneously implemented to three Italian study samples, each one constituted by two-hundred residential units sold in the years 2016-2017. The ability to generate a "unique" functional form for the three different territorial contexts considered, in which the relationships between the influencing factors and the selling prices are specified by different multiplicative coefficients that appropriately represent the market phenomena of each case study analyzed, is the main contribution of the proposed methodology. The method can provide support for private operators in the assessment of the territorial investment conveniences and for the public entities in the decisional phases regarding future tax and urban planning policies
A fuzzy expected value approach under generalized data envelopment analysis
Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models - fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models - and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers
A generalized fuzzy Multiple-Layer NDEA: An application to performance-based budgeting
Network data envelopment analysis (NDEA) is capable of considering operations and interdependence of a system’s component processes to measure efficiencies. There are numerous performance evaluation applications in which some indicators have hierarchical structures with a considerable number of sub-indicators. This problem of ignoring the hierarchical structure of indicators weakens the discrimination power of NDEA models and may result in inaccurate efficiency scores. In this paper we propose a generalized fuzzy Multiple-Layer NDEA (GFML-NDEA) model and GFML-NDEA-based composite indicators (GFML-NDEA-CI) to incorporate the hierarchical structures of indicators in the ambit of the particular two-stage NDEA models. To demonstrate the usefulness of the GFMLNDEA-CI model proposed, its application was tested by evaluating the efficiency of the performance-based budgeting (PBB) system in 14 governmental agencies in Iran. The comparative analysis results obtained from the GFML-NDEA-CI (multi-layer) model with those from the single-layer fuzzy NDEA-CI model indicate that the number of efficient decision-making units (DMUs) in the one-layer model is eight, whereas it is solely one DMU in the multi-layer model. The discrimination power of the multi-layer model proposed is significantly increased by observing that standard deviation of efficiency scores are increased by 41%, 61%, and 84% for possibility levels 0, 0.5, and 1, respectively. This is obtained while reducing information entropy, thus suggesting that the proposed model yields more reliable scores
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
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
Defuzzification of groups of fuzzy numbers using data envelopment analysis
Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the
original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may
exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved
relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed
CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals
fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships
A fuzzy DEA slacks-based approach
This paper deals with the problem of efficiency assessment using Data Envelopment Analysis (DEA) when the input and output data are given as fuzzy sets. In particular, a fuzzy extension of the measure of inefficiency proportions, a well-known slacks-based additive inefficiency measure, is considered. The proposed approach also provides fuzzy input and output targets. Computational experiences and comparison with other fuzzy DEA approaches are reported.The first author was partially supported by the research project MTM2017-89577-P (MINECO, Spain). The second author was partially supported by the Spanish Ministry of Economy and Competitiveness, grant AYA2016-75931-C2-1-P and from the ConsejerÃa de Educación y Ciencia, Spain (Junta de AndalucÃa, reference TIC-101). The third author acknowledges the financial support of the Spanish Ministry of Science, Innovation and Universities, grant PGC2018-095786-B-I00
Sustainable R&D portfolio assessment.
Research and development portfolio management is traditionally technologically and financially dominated, with little or no attention to the sustainable focus, which represents the triple bottom line: not only financial (and technical) issues but also human and environmental values. This is mainly due to the lack of quantified and reliable data on the human aspects of product/service development: usability, ecology, ethics, product experience, perceived quality etc. Even if these data are available, then consistent decision support tools are not ready available. Based on the findings from an industry review, we developed a DEA model that permits to support strategic R&D portfolio management. We underscore the usability of this approach with real life examples from two different industries: consumables and materials manufacturing (polymers).R&D portfolio management; Data envelopment analysis; Sustainable R&D;
Methodological Developments in Human Development Literature
The present paper is a review of methodological advancements in human development literature starting from 1990 till date. While highlighting the contribution of UNDP to the concept of human development and construction of HDI it mentions that the introduced concept and method of measurement is a huge qualitative improvement over the earlier concept of growth and per capita GDP measurement. Although the human development report started with a poor methodology, thanks to the galaxy of scholars for their untiring efforts and invaluable contributions in the successive years that enabled UNDP in refining its methodology to a large extent. There is no denying fact that there is no end to refinements, the purpose for which Mahbub ul Haq struggled in his entire life has been served
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