Failure to identify major target market and lack of cost management have shown to be negative for enterprise operations. To remedy these two problems information technologies have promised to support effective and efficent decision-making processing by providing accurate and real-time information, so as to assist front-line sales, to lower down costs, to create more profits and to further diversified the risks. In steel industry, the repeated purchasing frequency (associated with customer loyalty) has been identified as one of the major determinants due to the narrow target market and capital-intensive features existed in steel business. Steel businesses often involve international transactions, which have much higher uncertainties than domestic transactions. Therefore, the major risk control task is on the collection and analysis customers¡¦ creditability. More customer information leads to fewer risks. As such, we believe that by applying the methodology of collecting and analyzing information through information technologies, the decision makers will be more confident in making correct decisions, which in turn help to generate profits. In this thesis, we develop a data warehouse system in support of order procurement. We begin by identifying the types of information that is crucial in making order procurement decision by consulting with the experienced international and domestic front-line sales managers. The different types of information are prioritized by applying Analytic Hierarchy Process (AHP) approach. We then construct a datawarehouse model that encompasses these information. A system prototype is finally developed to demonstrate the feasiblity of our approach.

Spatiotemporal dynamics of resource availability can produce markedly different patterns of landscape utilization which necessitates studying habitat selection across biologically relevant extents. Feral pigs (Sus scrofa) are a prolifically expanding, generalist species and researchers have yet to understand fundamental drivers of space use in agricultural landscapes within the United States. To study multi-scale habitat selection patterns, I deployed 13 GPS collars on feral pigs within the Mississippi Alluvial Valley. I estimated resource selection using mixed-effects models to determine how feral pigs responded to changes in forage availability and incorporated those results with autocorrelated kernel density home range estimates. My results indicated season-specific habitat functional responses to changes in agricultural phenology and illustrated the interdependencies of landscape composition, hierarchical habitat selection, and habitat functional responses. These results indicate fundamental drivers of feral pig spatial distributions in an agricultural landscape which I used to predict habitat use to direct feral pig management.

This research is motivated by the need for considering lot sizing while accepting customer orders in a make-to-order (MTO) environment, in which each customer order must be delivered by its due date. Job shop is the typical operation model used in an MTO operation, where the production planner must make three concurrent decisions; they are order selection, lot size, and job schedule. These decisions are usually treated separately in the literature and are mostly led to heuristic solutions. The first phase of the study is focused on a formal definition of the problem. Mathematical programming techniques are applied to modeling this problem in terms of its objective, decision variables, and constraints. A commercial solver, CPLEX is applied to solve the resulting mixed-integer linear programming model with small instances to validate the mathematical formulation. The computational result shows it is not practical for solving problems of industrial size, using a commercial solver. The second phase of this study is focused on development of an effective solution approach to this problem of large scale. The proposed solution approach is an iterative process involving three sequential decision steps of order selection, lot sizing, and lot scheduling. A range of simple sequencing rules are identified for each of the three sub-problems. Using computer simulation as the tool, an experiment is designed to evaluate their performance against a set of system parameters. For order selection, the proposed weighted most profit rule performs the best. The shifting bottleneck and the earliest operation finish time both are the best scheduling rules. For lot sizing, the proposed minimum cost increase heuristic, based on the Dixon-Silver method performs the best, when the demand-to-capacity ratio at the bottleneck machine is high. The proposed minimum cost heuristic, based on the Wagner-Whitin algorithm is the best lot-sizing heuristic for shops of a low demand-to-capacity ratio. The proposed heuristic is applied to an industrial case to further evaluate its performance. The result shows it can improve an average of total profit by 16.62%. This research contributes to the production planning research community with a complete mathematical definition of the problem and an effective solution approach to solving the problem of industry scale.

The present thrust in the electronics industry towards integrating multiple functions on a single chip while operating at very high frequencies has highlighted the need for efficient Electronic Design Automation (EDA) tools to shorten the design cycle and capture market windows. However, the increasing complexity in modern circuit design has made simulation a computationally cumbersome task. The notion of model order reduction has emerged as an effective tool to address this difficulty. Typically, there are numerous approaches and several issues involved in the implementation of model-order reduction techniques. Among the important ones of those issues is the problem of determining a suitable order (or size) for the reduced system. An optimal order would be the minimal order that enables the reduced system to capture the behavior of the original (more complex and larger) system up to a user-defined frequency. The contribution presented in this thesis describes a new approach aimed at determining the order of the reduced system. The proposed approach is based on approximating the impulse response of the original system in the time-domain. The core methodology in obtaining that approximation is based on numerically inverting the Laplace-domain of the representation of the impulse response from the complex-domain (s-domain) into the time-domain. The main advantage of the proposed approach is that it allows the order selection algorithm to operate directly on the time-domain form of the impulse response. It is well-known that numerically generating the impulse response in the time-domain is very difficult and its not impossible, since it requires driving the original network with the Dirac-delta function, which is a mathematical abstraction rather than a concrete waveform that can be implemented on a digital computer. However, such a difficulty is avoided in the proposed approach since it uses the Laplace-domain image of the impulse response to obtain its time-domain representation. The numerical simulations presented in the thesis demonstrate that using the time-domain waveform of the impulse response, computed using the proposed approach and properly filtered with a Butterworth filter, guides the order selection algorithm to select a smaller order, i.e., the reduced system becomes more compact in size. The phrase "smaller or more compact" in this context refers to the comparison with existing techniques currently in use, which seek to generate some form of time-domain approximations for the impulse response through driving the original network with pulse-shaped function (e.g., Gaussian pulse).

La finesse de la résolution spectrale et spatiale des images hyperspectrales en font des données de très grande dimension. C'est également le cas d'autres types de données, où leur taille tend à augmenter pour de plus en plus d'applications. La complexité des données provenant de l'hétérogénéité spectrale et spatiale, de la non gaussianité du bruit et des processus physiques sous-jacents, renforcent la richesse des informations présentes sur une image hyperspectrale. Exploiter ces informations demande alors des outils statistiques adaptés aux grandes données mais aussi à leur nature non gaussienne. Des méthodes reposant sur la théorie des matrices aléatoires, théorie adaptée aux données de grande dimension, et reposant sur la robustesse, adaptée aux données non gaussiennes, sont ainsi proposées dans cette thèse, pour des applications à l'imagerie hyperspectrale. Cette thèse propose d'améliorer deux aspects du traitement des images hyperspectrales : l'estimation du nombre d'endmembers ou de l'ordre du modèle et le problème du démélange spectral. En ce qui concerne l'estimation du nombre d'endmembers, trois nouveaux algorithmes adaptés au modèle choisi sont proposés, le dernier présentant de meilleures performances que les deux autres, en raison de sa plus grande robustesse.Une application au domaine de la finance est également proposée. Pour le démélange spectral, trois méthodes sont proposées, qui tiennent comptent des diff érentes particularités possibles des images hyperspectrales. Cette thèse a permis de montrer que la théorie des matrices aléatoires présente un grand intérêt pour le traitement des images hyperspectrales. Les méthodes développées peuvent également s'appliquer à d'autres domaines nécessitant le traitement de données de grandes dimensions. / Hyperspectral imaging generates large data due to the spectral and spatial high resolution, as it is the case for more and more other kinds of applications. For hyperspectral imaging, the data complexity comes from the spectral and spatial heterogeneity, the non-gaussianity of the noise and other physical processes. Nevertheless, this complexity enhances the wealth of collected informations, that need to be processed with adapted methods. Random matrix theory and robust processes are here suggested for hyperspectral imaging application: the random matrix theory is adapted to large data and the robustness enables to better take into account the non-gaussianity of the data. This thesis aims to enhance the model order selection on a hyperspectral image and the unmixing problem. As the model order selection is concerned, three new algorithms are developped, and the last one, more robust, gives better performances. One financial application is also presented. As for the unmixing problem, three methods that take into account the peculierities of hyperspectral imaging are suggested. The random matrix theory is of great interest for hyperspectral image processing, as demonstrated in this thesis. Differents methods developped here can be applied to other field of signal processing requiring the processing of large data.

Abstract of thesis submitted in partial fulfilment of the requirements for the Degree of Master of Commerce and Management Dynamics of macroeconomic variables in Fiji : a cointegrated VAR analysis By Shiu Raj Singh The objective of this study is to examine how macroeconomic variables of Fiji inter-relate with aggregate demand and co-determine one another using a vector autoregression (VAR) approach. This study did not use a prior theoretical framework but instead used economic justification for selection of variables. It was found that fiscal policy, which is generally used as a stabilisation tool, did not have a positive effect on real Gross Domestic Product (GDP) growth in the short term. Effects on GDP growth were positive over the long term but not statistically significant. Furthermore, expansionary fiscal policy caused inflationary pressures. Fiji has a fixed exchange rate regime, therefore, it was expected that the focus of monetary policy would be the maintenance of foreign reserves. It was, however, found that monetary expansion in the short term resulted in positive effects on real GDP growth and resulted in inflation. The long term effects of monetary policy on real GDP growth were negative, which are explained by the fixed exchange rate regime, endogenous determination of money supply by the central bank, an unsophisticated financial market and, perhaps, an incomplete transmission of the policy. Both merchandise trade and visitor arrivals growth were found to positively contribute to short term and long term economic growth. Political instability was found not to have significant direct effects on real GDP growth but caused a significant decline in visitor arrivals which then negatively affected economic growth in the short term.