Today, there is little understanding of how local decisions and disturbances impact the global performance of the supply chain. In this research, we attempt to gain insight about such relationship using multi-resolution hybrid models. To this end, a novel hybrid architecture and methodology consisting of simulation (system dynamic and discrete-event) and optimization modules is proposed. The proposed methodology, applicable to general supply chains, is divided into fours stages: plan stability analysis (Stage I), plan optimization (Stages II), schedule optimization (Stage III) and concurrent decision evaluation (Stage IV). Functional and process models of the proposed architecture are specified using formal IDEF tools. A realistic three-echelon conjoined supply chain system characterized by communicative and collaborative (VMI) configurations is analyzed in this research. Comprehensive SD models of each player of the supply chain have been developed. General conditions of the stability (settings of control parameters that produce stable response) are derived using z-transformation techniques (Stage I), and insights into the behavior of the supply chain are gained. Next, a novel method for the integration of the stability analysis with performance analysis (optimization) is presented (Stage II) by employing the derived stability conditions derived as additional constraints within the optimization models. Next, in Stage III, the scheduling at each chain partner using discrete-event simulation (DES) modeling techniques is addressed. In Stage IV, the optimality of the SD control parameters (from Stage II) and DES operational policies (from Stage III) for each member are concurrently evaluated by integrating the SD and DES models. Evaluation in Stage IV is performed to better understand the global consequence of the locally optimal decisions determined at each supply chain member. A generic infrastructure has been developed using High Level Architecture (HLA) to integrate the distributed decision and simulation models. Experiments are conducted to demonstrate the proposed architecture for the analysis of distributed supply chains. The progressions of cost based objective function from Stages I-III are compared with that from the concurrent evaluation in Stage IV. Also the ability of the proposed methodology to capture the effect of dynamic perturbations within the supply chain system is illustrated.

Time series forecasting is an important area of forecasting in which past observations of the same variable are collected and analyzed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. This modeling approach is particularly useful when little knowledge is available on the underlying data generating process or when there is no satisfactory explanatory model that relates the prediction variable to other explanatory variables. Time series can be modeled in a variety of ways e.g. using exponential smoothing techniques, regression models, autoregressive (AR) techniques, moving averages (MA) etc. Recent research activities in forecasting also suggested that artificial neural networks can be used as an alternative to traditional linear forecasting models. This study will, along the lines of an existing study in the literature, investigate the use of a hybrid approach to time series forecasting using both linear and neural network models. The proposed methodology consists of two basic steps. In the first step, a linear model is used to analyze the linear part of the problem and in the second step a neural network model is developed to model the residuals from the linear model. The results from the neural network can then be used to predict the error terms for the linear model. This means that the combined forecast of the time series will depend on both models. Following an overview of the models, empirical tests on real world data will be performed to determine the forecasting performance of such a hybrid model. Results have indicated that depending on the forecasting period, it might be worthwhile to consider the use of a hybrid model. / Thesis (M.Sc. (Computer Science))--North-West University, Vaal Triangle Campus, 2010.

Time series forecasting is an important area of forecasting in which past observations of the same variable are collected and analyzed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. This modeling approach is particularly useful when little knowledge is available on the underlying data generating process or when there is no satisfactory explanatory model that relates the prediction variable to other explanatory variables. Time series can be modeled in a variety of ways e.g. using exponential smoothing techniques, regression models, autoregressive (AR) techniques, moving averages (MA) etc. Recent research activities in forecasting also suggested that artificial neural networks can be used as an alternative to traditional linear forecasting models. This study will, along the lines of an existing study in the literature, investigate the use of a hybrid approach to time series forecasting using both linear and neural network models. The proposed methodology consists of two basic steps. In the first step, a linear model is used to analyze the linear part of the problem and in the second step a neural network model is developed to model the residuals from the linear model. The results from the neural network can then be used to predict the error terms for the linear model. This means that the combined forecast of the time series will depend on both models. Following an overview of the models, empirical tests on real world data will be performed to determine the forecasting performance of such a hybrid model. Results have indicated that depending on the forecasting period, it might be worthwhile to consider the use of a hybrid model. / Thesis (M.Sc. (Computer Science))--North-West University, Vaal Triangle Campus, 2010.

Managing customer credit is an important issue for each commercial bank; therefore, banks take great care when dealing with customer loans to avoid any improper decisions that can lead to loss of opportunity or financial losses. The manual estimation of customer creditworthiness has become both time- and resource-consuming. Moreover, a manual approach is subjective (dependable on the bank employee who gives this estimation), which is why devising and implementing programming models that provide loan estimations is the only way of eradicating the ‘human factor’ in this problem. This model should give recommendations to the bank in terms of whether or not a loan should be given, or otherwise can give a probability in relation to whether the loan will be returned. Nowadays, a number of models have been designed, but there is no ideal classifier amongst these models since each gives some percentage of incorrect outputs; this is a critical consideration when each percent of incorrect answer can mean millions of dollars of losses for large banks. However, the LR remains the industry standard tool for credit-scoring models development. For this purpose, an investigation is carried out on the combination of the most efficient classifiers in credit-scoring scope in an attempt to produce a classifier that exceeds each of its classifiers or components. In this work, a fusion model referred to as ‘the Classifiers Consensus Approach’ is developed, which gives a lot better performance than each of single classifiers that constitute it. The difference of the consensus approach and the majority of other combiners lie in the fact that the consensus approach adopts the model of real expert group behaviour during the process of finding the consensus (aggregate) answer. The consensus model is compared not only with single classifiers, but also with traditional combiners and a quite complex combiner model known as the ‘Dynamic Ensemble Selection’ approach. As a pre-processing technique, step data-filtering (select training entries which fits input data well and remove outliers and noisy data) and feature selection (remove useless and statistically insignificant features which values are low correlated with real quality of loan) are used. These techniques are valuable in significantly improving the consensus approach results. Results clearly show that the consensus approach is statistically better (with 95% confidence value, according to Friedman test) than any other single classifier or combiner analysed; this means that for similar datasets, there is a 95% guarantee that the consensus approach will outperform all other classifiers. The consensus approach gives not only the best accuracy, but also better AUC value, Brier score and H-measure for almost all datasets investigated in this thesis. Moreover, it outperformed Logistic Regression. Thus, it has been proven that the use of the consensus approach for credit-scoring is justified and recommended in commercial banks. Along with the consensus approach, the dynamic ensemble selection approach is analysed, the results of which show that, under some conditions, the dynamic ensemble selection approach can rival the consensus approach. The good sides of dynamic ensemble selection approach include its stability and high accuracy on various datasets. The consensus approach, which is improved in this work, may be considered in banks that hold the same characteristics of the datasets used in this work, where utilisation could decrease the level of mistakenly rejected loans of solvent customers, and the level of mistakenly accepted loans that are never to be returned. Furthermore, the consensus approach is a notable step in the direction of building a universal classifier that can fit data with any structure. Another advantage of the consensus approach is its flexibility; therefore, even if the input data is changed due to various reasons, the consensus approach can be easily re-trained and used with the same performance.

Historically turbulence modeling has been sharply divided into Reynolds averaged Navier-Stokes (RANS), in which all the turbulent scales of motion are modeled, and large-eddy simulation (LES), in which only a portion of the turbulent spectrum is modeled. In recent years there have been numerous attempts to couple these two approaches either by patching RANS and LES calculations together (zonal methods) or by blending the two sets of equations. In order to create a proper bridging model, that is, a single set of equations which captures both RANS and LES like behavior, it is necessary to place both RANS and LES in a more general framework.The goal of the current work is threefold: to provide such a framework, to demonstrate how the Flow Simulation Methodology (FSM) fits into this framework, and to evaluate the strengths and weaknesses of the current version of the FSM. To do this, first a set of filtered Navier-Stokes (FNS) equations are introduced in terms of an arbitrary generalized filter. Additional exact equations are given for the second order moments and the generalized subfilted dissipation rate tensor. This is followed by a discussion of the role of implicit and explicit filters in turbulence modeling.The FSM is then described with particular attention to its role as a bridging model. In order to evaluate the method a specific implementation of the FSM approach is proposed. Simulations are presented using this model for the case of separating flow over a "hump" with and without flow control. Careful attention is paid to error estimation, and, in particular, how using flow statistics and time series affects the error analysis. Both mean flow and Reynolds stress profiles are presented, as well as the phase averaged turbulent structures and wall pressure spectra. Using the phase averaged data it is possible to examine how the FSM partitions the energy between the coherent resolved scale motions, the random resolved scale fluctuations, and the subfilter quantities.The method proves to be qualitatively successful at reproducing large turbulent structures. However, like other hybrid methods, it has difficulty in the region where the model behavior transitions from RANS to LES> Consequently the phase averaged structures reproduce the experiments quite well, and the forcing does significantly reduce the length of the separated region. Nevertheless, the recirculation length is signficantly too large for all cases.Overall the current results demonstrate the promise of bridging models in general and the FSM in particular. However, current bridging techniques are still in their infancy. There is still important progress to be made and it is hoped that this work points out the more important avenues for exploration.

Orientador: José Luiz Gasche / Resumo: Na atualidade, um compressor precisa ter elevada eficiência para ser competitivo no mercado. Como o escoamento de refrigerante nas válvulas é uma das maiores fontes de perdas termodinâmicas, melhorias no seu projeto podem aumentar significativamente a eficiência do compressor. Simular computacionalmente válvulas de compressores utilizando modelos tridimensionais completos, além de computacionalmente caro, demanda muito tempo e, por essa razão, pesquisadores buscam metodologias simplificadas para realizar a tarefa de predizer a dinâmica das válvulas. Aqui avaliamos a aplicabilidade de um modelo híbrido para simular computacionalmente o comportamento dinâmico de um modelo frequentemente usado como válvula de sucção. Neste modelo, resolvemos o problema do escoamento tridimensional em regime transiente pela válvula aplicando o método de volumes finitos para escoamento compressível e isotérmico e a dinâmica da válvula usando um modelo massa-mola-amortecedor com um grau de liberdade. Comparamos os resultados do modelo híbrido com aqueles obtidos de um modelo tridimensional completo para a estrutura da válvula usando como parâmetros o tempo de processamento, deslocamento da válvula, força sobre a válvula e distribuições de pressão e de velocidade do escoamento. Validamos os modelos numéricos com resultados experimentais. A boa concordância entre os resultados indicam que a redução de tempo obtida pelo modelo híbrido é pequena (15% em média) e não justifica seu uso. / Mestre

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Submitted by Allan Demétrio Sales de Lima Dias null (allan.demetrio@gmail.com) on 2016-10-07T18:29:32Z No. of bitstreams: 1 Dissertação_Allan.pdf: 5465196 bytes, checksum: 143e2c7ec7ff9f02db2c70094e3030ee (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-10-13T20:25:00Z (GMT) No. of bitstreams: 1 dias_adsl_me_ilha.pdf: 5465196 bytes, checksum: 143e2c7ec7ff9f02db2c70094e3030ee (MD5) / Made available in DSpace on 2016-10-13T20:25:00Z (GMT). No. of bitstreams: 1 dias_adsl_me_ilha.pdf: 5465196 bytes, checksum: 143e2c7ec7ff9f02db2c70094e3030ee (MD5) Previous issue date: 2016-08-30 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Na atualidade, um compressor precisa ter elevada eficiência para ser competitivo no mercado. Como o escoamento de refrigerante nas válvulas é uma das maiores fontes de perdas termodinâmicas, melhorias no seu projeto podem aumentar significativamente a eficiência do compressor. Simular computacionalmente válvulas de compressores utilizando modelos tridimensionais completos, além de computacionalmente caro, demanda muito tempo e, por essa razão, pesquisadores buscam metodologias simplificadas para realizar a tarefa de predizer a dinâmica das válvulas. Aqui avaliamos a aplicabilidade de um modelo híbrido para simular computacionalmente o comportamento dinâmico de um modelo frequentemente usado como válvula de sucção. Neste modelo, resolvemos o problema do escoamento tridimensional em regime transiente pela válvula aplicando o método de volumes finitos para escoamento compressível e isotérmico e a dinâmica da válvula usando um modelo massa-mola-amortecedor com um grau de liberdade. Comparamos os resultados do modelo híbrido com aqueles obtidos de um modelo tridimensional completo para a estrutura da válvula usando como parâmetros o tempo de processamento, deslocamento da válvula, força sobre a válvula e distribuições de pressão e de velocidade do escoamento. Validamos os modelos numéricos com resultados experimentais. A boa concordância entre os resultados indicam que a redução de tempo obtida pelo modelo híbrido é pequena (15% em média) e não justifica seu uso. / Currently, a refrigeration compressor must have high efficiency in order to become competitive in the market. Since the refrigerant flow through the valves is one of the largest sources of thermodynamic losses, improvements in their design can significantly increase the compressor efficiency. Computational simulations of compressors valves using - complete three-dimensional models, besides computationally expensive, is time consuming. For this reason, researchers always look for simplified methodologies for predicting the valves dynamics. Here, we evaluate the applicability of a hybrid model to computationally simulate the dynamics of a model frequently used as suction valve. In this model, we solve the problem of the three-dimensional unsteady flow through the valve by applying the finite volume method for compressible and isothermal flow and the dynamics of the valve by using a mass-spring-damper model with one degree of freedom. We compare the results from the hybrid model with those obtained with a complete three-dimensional model for the structure using as parameters the processing time, valve displacement, force acting on the valve, and pressure and velocity fields of the flow. We validate the numerical models with experimental data. The good agreement between the results indicates that the reduction of the processing time obtained by using the hybrid model is small (15% in average) and does not justify its use.

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The high social costs associated with bankruptcy have spurred searches for better theoretical understanding and prediction capability. In this paper, we investigate a hybrid approach to bankruptcy prediction, using a genetic programming algorithm to construct a bankruptcy prediction model with variables from a rough sets model derived in prior research. Both studies used data from 291 US public companies for the period 1991 to 1997. The second stage genetic programming model developed in this research consists of a decision model that is 80% accurate on a validation sample as compared to the original rough sets model which was 67% accurate. Additionally, the genetic programming model reveals relationships between variables that are not apparent in either the rough sets model or prior research. These findings indicate that genetic programming coupled with rough sets theory can be an efficient and effective hybrid modeling approach both for developing a robust bankruptcy prediction model and for offering additional theoretical insights.