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Under some conditions in real world, precise parameters and/or initial values of dynamic systems are hard to be determined. Fuzzy Differential Equation (FDE) is a powerful tool to model dynamical systems with the uncertainty of impreciseness. This thesis presents the first numerical solution for Fuzzy Differential Equations with multiple fuzzy parameters and initial Values (FDEPIV) problems. Previous approaches for solving the FDEs only focused on FDEs with single fuzzy condition. In this thesis, we applied the proper fuzzy arithmetic on Runge-Kutta method for solving the FDEPIV problems with multiple fuzzy parameters and initial conditions. Furthermore, comparing with directly applying the extension principle in solving FDEPIV, the complexity of the proposed method is much lower, and parallelization of the proposed algorithm is feasible. Numerical examples of the FDEPIV problems are presented to demonstrate the effectiveness of the proposed method.

Under some conditions in real world, precise parameters and/or initial values of dynamic systems are hard to be determined. Fuzzy Differential Equation (FDE) is a powerful tool to model dynamical systems with the uncertainty of impreciseness. This thesis presents the first numerical solution for Fuzzy Differential Equations with multiple fuzzy parameters and initial Values (FDEPIV) problems. Previous approaches for solving the FDEs only focused on FDEs with single fuzzy condition. In this thesis, we applied the proper fuzzy arithmetic on Runge-Kutta method for solving the FDEPIV problems with multiple fuzzy parameters and initial conditions. Furthermore, comparing with directly applying the extension principle in solving FDEPIV, the complexity of the proposed method is much lower, and parallelization of the proposed algorithm is feasible. Numerical examples of the FDEPIV problems are presented to demonstrate the effectiveness of the proposed method.

This thesis describes a procedure to measure the transient effects in a fuel cell air delivery system. These methods were applied to model the 20 kW automotive fuel cell system that was used in Animul H2, a fuel cell-battery hybrid sedan developed by a group of engineering students at Virginia Tech. The air delivery system included the air compressor, the drive motor for the compressor, the motor controller, and any plumbing between the fuel cell inlet and the compressor outlet. The procedure was to collect data from a series of tests of the air delivery system with no load (zero outlet pressure) and at several loads. The air compressor speed, outlet pressure, and motor controller current were measured in response to a variety of speed requests. This data was fit to transfer functions relating the compressor speed, outlet pressure, or motor controller current to the speed request. The fits were found using a least squares optimization technique. After the experimental model was developed, it was augmented with an analytical model of the rest of the fuel cell system. The mass flow of the air was determined from the air compressor speed and outlet pressure with the compressor map. The fuel cell current was found by assuming a constant stoichiometric ratio. The power out of the fuel cell was calculated from the fuel cell current and the pressure with the polarization curve. The model of the fuel cell system was implemented in Matlab/Simulink. Several open and closed loop simulations were run to test the functionality of the fuel cell system model. The gross and net powers of the fuel cell system were found as a function of the compressor operating speed. The time it took for the system to come up to power as a function of idle speed was also found. A PID controller was implemented to allow the system to track a reference power request. The key contributions of this work were to develop a method to test the air delivery system to determine the dynamics of the system, to develop a model based on these tests and some analytical knowledge of fuel cells, and to use the model to simulate the operation and control of a fuel cell system. / Master of Science

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).

No description available.

No description available.