Thesis (Ph.D.)--University of Delaware, 2007. / Principal faculty advisor: Jeremy S. Edwards, Dept. of Chemical Engineering. Includes bibliographical references.

Thesis (M.S. in Modeling, Virtual Environments, and Simulation (MOVES))--Naval Postgraduate School, December 2009. / Thesis Advisor(s): Buss, Arnold. Second Reader: Meyer, David. "December 2009." Description based on title screen as viewed on January 28, 2010. Author(s) subject terms: Discrete Event Simulation, airlift operation. Includes bibliographical references (p. 145-147). Also available in print.

Thesis (M.S.)--Brigham Young University. Dept. of Civil and Environmental Engineering, 2008. / Includes bibliographical references (p. 51-54).

Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Statistics." Includes bibliographical references (p.37-38).

No description available.

Thesis (Master)--İzmir Institute of Technology, İzmir, 2005. / Keywords: Geothermal energy, multi criteria decision method, stochastic methods, Monte Carlo method. Includes bibliographical references (leaves.93-98).

Thesis (M.S.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains viii, 45 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 44-45).

Ship collision accidents are rare events but pose huge threat to human lives, assets, and the environment. Collision resistance of ships is usually assessed in terms of ship structural response such as member displacement, energy dissipation and the extent of damage. Many researchers have sought for effective models that compute ship stochastic response during collisions by considering the variability of collision scenario parameters. However, the models were limited by the capability of the collision computational models and did not completely capture collision scenario, and material and geometric uncertainties. In addition, the simplified models capturing the input-response relationships of the ship structural impact mechanics are in implicit forms which makes them unsuitable for assessing the performance of structural design specifications in collisions. Furthermore, with increasing ship passages in the Arctic region, the probabilities of ship-iceberg interactions are increasing, highlighting the need to focus on risk based ship designs. In this research, a conceptual stochastic modelling framework is developed for performance characterisation and quantitative risk assessment of ship-ship and ship-iceberg collisions. In this direction, an interface for automated stochastic finite element computations was developed to model ship structural resistance in reference collision scenarios. The stochastic structural response was characterised based on the onset of the ship structural failure. The focus was initially on ship-ship collisions to quantify the uncertainties experimentally and to characterise the performance for a variety of striking ships. The framework was then extended to consider probabilistic performance measures in ship-iceberg collisions. The computationally intensive collision response models were captured with efficient surrogate representations so that the performance measures can be obtained with gradient based reliability approaches. The most probable input design sets for the response distribution were sampled with Latin Hypercube models. The probabilistic performance measures were also combined with available collision frequency models from literature for risk computations and to demonstrate the risk tolerance measures. The framework underlines the significance of different risk components, providing valuable guidance for improving risk-based ship designs. Although, a double-hull crude oil carrier is presented as the struck ship, the approach can be readily extended to characterise the performance and risk of other ship structures in collisions.

This thesis is concerned with quantifying the dynamical role of stochasticity in models of recurrent epidemics. Although the simulation of stochastic models can accurately capture the qualitative epidemic patterns of childhood diseases, there is still considerable discussion concerning the basic mechanisms generating these patterns. The novel aspect of this thesis is the use of analytic methods to quantify the results from simulations. All the models are formulated as continuous time Markov processes, the temporal evolutions of which is described by a master equation. This is expanded in the inverse system size, which decomposes the full stochastic dynamics into a macroscopic part, described by deterministic equations, plus a stochastic fluctuating part. The first part examines the inclusion of non-exponential latent and infectious periods into the the standard susceptible-infectious-recovered model. The method of stages is used to formulate the problem as a Markov process and thus derive a power spectrum for the stochastic oscillations. This model is used to understand the dynamics of whooping cough, which we show to be the mixture of an annual limit cycle plus resonant stochastic oscillations. This limit cycle is generated by the time-dependent external forcing, but we show that the spectrum is close to that predicted by the unforced model. It is demonstrated that adding distributed infectious periods only changes the frequency and amplitude of the stochastic oscillations---the basic mechanisms remain the same. In the final part of this thesis, the effect of seasonal forcing is studied with an analysis of the full time-dependent master equation. The comprehensive nature of this approach allows us to give a coherent picture of the dynamics which unifies past work, but which also provides a systematic method for predicting the periods of oscillations seen in measles epidemics. In the pre-vaccination regime the dynamics are dominated by a period doubling bifurcation, which leads to large biennial oscillations in the deterministic dynamics. Vaccination is shown to move the system away from the biennial limit cycle and into a region where there is an annual limit cycle and stochastic oscillations, similar to whooping cough. Finite size effects are investigated and found to be of considerable importance for measles dynamics, especially in the biennial regime.

Firms' operations management decisions are often complicated by various types of uncertainties, ranging from micro level customer behavior to macro level economic conditions. Operating in the presence of uncertainties and volatilities is a challenging task, one that requires careful mathematical analysis and tailored treatment based on the uncertainty's characteristics. In this thesis we provide three distinct studies on managing stochastic uncertainty in dynamic marketplaces. The first study considers agents' dynamic interactions in a large matching market. A pair needs to inspect for their compatibility in order to form a match. We study a type of market failure called 'information deadlock' that may arise when pairs are only willing to inspect their most preferred prevailing partner. Under information deadlock, a large fraction of agents wait in the market for long (if not forever) in spite of there being opportunities remaining in their consideration sets. Using advanced tools in statistical physics and random graph theory, we derive how the size of the deadlock is affected by the market's primitives. We also show that information deadlock is prevalent in a wide range of markets. Our second study tackles a service firm's problem of choosing between a safe service mode and a risky service mode when serving a customer who might probabilistically churn. One key behavioral feature of the customer that we consider is named recency bias --- his happiness with the firm (that crucially determines his churn risk at the time) depends more heavily on his more recent experience. We show, by solving a stochastic control problem, that the firm should be risk-averse when the customer is marginally satisfied and risk-seeking when the customer is marginally unsatisfied. The optimal sandwich policy can significantly outperform the naive myopic policy in terms of customer lifetime value. Our third study deals with a dual sourcing problem under fluctuating economic conditions. We model this via an underlying Markov modulated state-of-the-world which affects the two suppliers’ cost structures, capacity limits and demands. We develop two approaches to show how the optimal combined ordering strategy from the two suppliers, along with a salvaging policy, can be efficiently computed, and characterize the relatively simple structure of the optimal policies. Interestingly, we find that the firm can, by exploiting the dual sourcing options, benefit from increased environmental volatilities that affect the suppliers’ cost structures or capacity limits.