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Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.

In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

This paper aims to investigate the issue of performance measurement of social enterprises with main particular focus on economic performance approach reflecting local aspect, since the local aspect plays a significant role in social entrepreneurship concept. Thus, comparison of two instruments that consider local aspects: local multiplier 3 (LM3) and Input-Output analysis is examined. Input-Output analysis is empirically used to calculate social enterprises’ impact on the Czech economy and to confirm the assumption to be more suitable instrument for economic performance measurement of social enterprises in comparison to local multiplier 3 method.

The research adopted two-phase approach. At first a qualitative approach was used to compare the two selected instruments (LM3 and Input-Output analysis). Consequently, a quantitative research was applied to determine the impact of social enterprises’ activities on the economies of individual regions. The research was carried out on a sample of social enterprises (143 subjects) in the Czech Republic covering the year 2015. For this approach, Symmetric Input-Output tables and multipliers were calculated and revenue data from each observed social enterprise were used.

The findings of the research indicate effects of social enterprises’ activities on the economy in individual regions of the Czech Republic. The results clearly show that the impact of social enterprises on the Czech economy is not negligible. Calculated impacts differ in sectors as well as in regions. Besides, of these findings, the assumption that the Input-Output analysis is more appropriate instrument for economic performance measurement of social enterprises, especially in situation of a larger sample of social enterprises in different regions, in comparison to LM3 method, was confirmed.

The limitations of this study is the dependence on the Symmetric Input-Output tables, which are usually published by local statistical offices and it takes some time to construct them. A more significant limitation and future research direction relates to the lack of generally accepted measurement framework in the sector of social enterprises and lack of the data, especially quantitative, in the Czech Republic.

The findings of the research represent a significant contribution to the process chain of understanding the role of social enterprises in (local) economy. The lack of proven impact of social enterprises to economy is considered as important limit of this sector development, not only from the perspective of investors and donors but also from the perspective of policymakers and even social enterprises themselves. The Input-Output analysis can be applied for any region of any country and can contribute to reduce scepticism about economic performance of social enterprises, thus to support putting social and environmental goals of social enterprises into practice.

This research has been carried out on the so far largest identified sample of social enterprises in the Czech Republic and provides data for discourse among stakeholders about social enterprises’ impact; thus, it enables to understand their impact and make capable to enlarge their support network and development.

This research is a pioneering attempt to provide data about economic impact of social enterprises using Input-Output analysis in the Czech Republic. No previous research identifies such a large sample of Czech social enterprises and collects quantitative data about them, as well as considers Input-Output analysis as an option instead of LM3 method. The effort of this research has been to react on a research gap in studies related to the impact of social enterprises (reflecting local aspect). This paper could be understood as beneficial not only from the perspective of the Czech Republic but also worldwide.

University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation.

This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs.

Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score.

This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

The purpose of this paper is to measure technical efficiency of Indian thermal power sector employing the recent by-production approach.

The by-production approach is used in conjunction with data from the Central Electricity Authority (CEA) of India to compute the output-based Färe, Grosskopf, Lovell (FGL) efficiency index and its decomposition into productive and environmental efficiency indexes for the ITPPs

The authors show that given the aggregated nature of data on coal reported by CEA, CEA’s computation of CO₂ emissions through a deterministic linear formula that does not distinguish between different coal types and the tiny share of oil in coal-based power plants, the computed output-based environmental efficiency indexes are no longer informative. Meaningful measurement of environmental efficiency using CEA data is possible only along the dimension of the coal input. Productive efficiency is positively associated with the engineering concept of thermodynamic/energy efficiency and is also high for power plants with high operating availabilities reflecting better management and O&M practices. Both these factors are high for private and centrally owned as opposed to state-owned power-generating companies. The example of Sipat demonstrates the importance of (ultra)supercritical technologies in increasing productive and thermodynamic efficiencies of the ITPPs, while also reducing CO₂ emitted per unit of the net electricity generated.

This paper uses the by-production approach for the first time to measure technical efficiency of ITPPs and highlights how the nature of the Indian data impacts on efficiency measurement.

This study aims to present the cost and revenue efficiency evaluation models in data envelopment analysis in the presence of fuzzy inputs, outputs and their prices that the prices are also fuzzy. This study applies the proposed approach in the energy sector of the oil industry.

This study proposes a value-based technology according to fuzzy input-cost and revenue-output data, and based on this technology, the authors propose an approach to calculate fuzzy cost and revenue efficiency based on a directional distance function approach. These papers incorporated a decision-maker’s (DM) a priori knowledge into the fuzzy cost (revenue) efficiency analysis.

This study shows that the proposed approach obtains the components of fuzzy numbers corresponding to fuzzy cost efficiency scores in the interval [0, 1] corresponding to each of the decision-making units (DMUs). The models presented in this paper satisfies the most important properties: translation invariance, translation invariance, handle with negative data. The proposed approach obtains the fuzzy efficient targets corresponding to each DMU.

In the proposed approach, by selecting the appropriate direction vector in the model, we can incorporate preference information of the DM in the process of evaluating fuzzy cost or revenue efficiency and this shows the efficiency of the method and the advantages of the proposed model in a fully fuzzy environment.

Embodied energy is the total amount of energy required to produce a product, and is significant because it occurs immediately and can be equal over the life cycle of a building to the transient requirements for operational energy. Methods for embodied energy analysis include process analysis, input‐output analysis and hybrid analysis. Proposes to improve the reliability of estimating embodied energy based on input‐output models by using an algorithm to extract systematically the most important energy paths for the “other construction” sector from an Australian input‐output model. Demonstrates the application of these energy paths to the embodied energy analysis of an individual commercial building, highlighting improvements in reliability due to the modification of energy paths with process analysis data. Compares materials and elements for the building, and estimates likely ranges of error.

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.