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Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

Integrated robust planning systems that cover all levels of SC hierarchy have become increasingly important. Strategic, tactical, and operational SC plans should not be generated in isolation to avoid infeasible and conflicting decisions. On the other hand, enterprise planning systems contain over millions of records that are processed in each planning iteration. In such enterprises, the ability to generate robust plans is vital to their success because such plans can save the enterprise resources that may otherwise have to be reserved for likely SC plan changes. A robust SC plan is valid in all circumstances and does not need many corrections in the case of interruption, error, or disturbance. Such a reliable plan is proactive as well as reactive. Proactivity can be achieved by forecasting the future events and taking them into account in planning. Reactivity is a matter of agility, the capability of keeping track of system behaviour and capturing alarming signals from its environment, and the ability to respond quickly to the occurrence of an unforeseen event. Modeling such a system behaviour and providing solutions after such an event is extremely important for a SC. This study focuses on integrated supply chain planning with stochastic dynamic considerations. An integrated tactical-operational model is developed and then segregated into two sub-models which are solved iteratively. A SC is a stochastic dynamic system whose state changes over time often in an unpredictable manner. As a result, the customer demand is treated as an uncertain parameter and is handled by exploiting scenario-based stochastic programming. The increase in the number of scenarios makes it difficult to obtain quick and good solutions. As such, a Branch and Fix algorithm is developed to segregate the stochastic model into isolated islands so as to make the computationally intractable problem solvable. However not all the practitioners, planners, and managers are risk neutral. Some of them may be concerned about the risky extreme scenarios. In view of this, the robust optimization approach is also adopted in this thesis. Both the solution robustness and model robustness are taken into account in the tactical model. Futhermore, the dynamic behaviour of a SC system is handled with the concept of Model Predictive Control (MPC).

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<p> We address a functional analysis-based method for the estimation of driving-forces from nonlinear dynamic systems in this thesis. The driving-forces account for the perturbation inputs or the irregular variations in the internal variables of a dynamic system. These inputs are hidden from the observer most of the time if not always. Reconstruction of such inputs when there is too little or no prior knowledge to build a mathematical model to describe the system's behavior is an important problem in many cases in physics and engineering. To this end, we propose a method for the recursive estimation of driving-forces without the availability of an analytic model of the unknown physical phenomenon. </p> <p> The underlying idea of the proposed estimator is to predict the observables onestep ahead of the current time instant, and then retrieve the driving-force from the prediction error. This idea is embodied by predicting the observables using a bank of echo state networks (ESN) in an online fashion, extracting the raw estimates from the prediction error, and then finally smoothing these estimates in separate adaptive filtering stages. The approach described herein distinguishes itself from the similar methods in the literature in its adaptivity and its greater immunity against varying environmental uncertainties. The adaptive nature of the estimator enables us to retrieve both slowly and rapidly varying driving-forces accurately in presence of model or sensor noises, which are illustrated by experiments in the subsequent chapters of this thesis. In particular, some chaotic/stochastic nonlinear models are studied in controlled experiments. The estimation quality of the proposed approach is judged with a reference to the Posterior Cramer-Rao Lower Bound as a theoretical lower limit on the estimation error. </p> <p> The Bayesian and Maximum-Likelihood (ML) methods are also studied for the estimation of driving-forces when partial or full information is available on the mathematical description of the unknown system. These methods serve as practical merits of assessment for the proposed driving-force estimator. Moreover, a direct performance comparison between the proposed estimator and a favorable estimation scheme of a similar kind is provided, which confirms the advantages of the proposed approach. The proposed method is tested on a real-world application on the extraction of sun's magnetic flux from the sunspot time series. It is illustrated that the results obtained by the proposed estimator are in close agreement with the results of two other analytical studies. </p> <p> Finally, a solution to a real problem in practice is proposed using the method. Specifically, extracting the signature of a small random target embedded in the sea surface is addressed using the live recorded data collected with the McMaster IPIX radar. This is the first specific realization of a radar scene analyzer for the cognitive radar reception in the literature to the author's best knowledge. </p> <p> The material in this thesis is presented in a sandwich thesis format, combining two peer reviewed, published journal articles, and another journal article that is prepared for submission. An additional chapter that provides the background material is included for the completeness of the presentation. </p> / Thesis / Doctor of Philosophy (PhD)

International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / Deutsche Telekom has been operating different flight models for several years. A Satellite Control Center (SCC) was designed and installed to support the operation of the satellite systems DFS Kopernikus and TV-Sat. The DFS Kopernikus system is composed of three flight models and the satellite system TV-Sat has one flight model. The aim was to design an SCC and ground stations in a way, enabling the operation of satellites and groundstations by only two operators at the main control room. The operators are well trained but not scientifically educated. The high integrated SCC supports the operators with a state of the art man-machine-interface. Software executes all necessary tasks for spacecraft- and ground station control. Interaction in front of communication equipment is not necessary. The operation of satellites is a business with a high risk potential. This paper presents the design of a Satellite Control Center with high system availability.

A maioria dos estudos realizados sobre moonpools existentes em embarcações sempre objetivou a redução das amplitudes de oscilação da água interna a estes, pois sua usual utilização é a passagem de linhas de produção, equipamentos e mergulhadores. Estes estudos mostram que com a mudança da geometria interna do moonpool é possível alterar o comportamento de oscilação da água interna ao mesmo. O presente trabalho tem como objetivo estudar o acoplamento entre o movimento vertical da água interna ao moonpool e a dinâmica em heave do corpo flutuante. Os adimensionais que regem o problema são apontados. Através das observações e conclusões em relação aos estudos numéricos e ensaios experimentais realizados, é proposto um modelo massa-mola-amortecedor representando o acoplamento entre o movimento vertical da água interna ao moonpool e a dinâmica em heave do corpo flutuante. / Most published studies related to moonpool existent in vessels always had as it main objective the reduction of vertical oscillation of the water inside the moonpool, as its usual function is the passage of production lines, equipments and divers. These studies show that, through the change of the moonpool\'s internal geometry, it is possible to modify the behavior of vertical oscillation of the water inside the moonpool. The present work has the objective of studying coupling between the vertical movement of the water inside the moonpool and the heave dynamics of the floating-body. The nondimensional numbers that guide the problem are indicated. Through the observations and conclusions in relation to the numerical studies and experimental analysis made, it is proposed a mass-spring-dumper model representing the coupling between the vertical movement of the water inside the moonpool and the heave dynamics of the floating-body.