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Stochastic two-stage programming, a main branch of stochastic programming, offers models and methods to find the optimal objective function and decision variables under uncertainty. This dissertation is concerned with developing an approximate procedure to solve the stochastic two-stage programming problem and applying it in relative field. Five methods used in evaluating the expected value of function for distribution problem are discussed and their basic characteristics and performances are compared to choose the most effective approach for use in a two-stage program. Then the stochastic two-stage programming solving method has been established with the combination of a genetic algorithm (GA) and point estimation (PE) procedure. This approach avoids the inherent limitations of other methods by using PE to estimate the expected value of recourse function and the GA to search optimal solution of the problem. To extend the advantage of GA the modified genetic algorithm (MGA) is built to improve the performance of GA. Finally, the whole procedure is used in several examples with different kinds of variable and linear or nonlinear style objective functions. A stochastic two-stage programming model for an aquifer management problem is set up with considering conductivity and local random recharge as the source of uncertainty in the system. The designed procedure includes the response matrix process that replaces the partial differential flow equation, Girinski potential process and a pre-setup process that makes the response matrix process application in general aquifer random field possible. Other chosen problems are solved with designed approach to illustrate the effects of uncertainty source in the stochastic programming model and compared with results with ones given in literatures.

In both physical and social sciences, we usually use controlled differential equation to model various continuous evolving system; describing how a response y relates to another process x called control. For regular controls x, the unique existence of the response y is guaranteed while it would never be the case for non-smooth controls via the classical approach. Besides, uniform closeness of controls may not imply closeness of their corresponding responses. Theory of rough paths provides a solution to both concerns. Since the creation of rough path theory, it enjoys a fruitful development and finds wide applications in stochastic analysis. In particular, rough path theory provides an effective method to study irregularity of curves and its geometric consequences in relation to integration of differential forms. In the chapter 2, we demonstrate the power of rough path theory in classical complex analysis by showing the rough path nature of the boundaries of a class of Holder's domains; as an immediate application, we extend the classical Gauss-Green's theorem. Until recently, there has been only limited research on applications of theory of rough paths to high dimensional geometry. It is clear to us that many geometric objects, in some senses appearing as solids, are actually comprised of filaments. In the chapter 3, two basic results in the theory of rough paths which will motivate later development of my thesis has been included. In the chapters 4 and 5, we identify a sensible way to do geometric calculus via those filaments (more precisely, space-filling rough paths) in dimension 3. In a recent joint work of Hambly and Lyons, they have shown that every rectifiable path can be completely characterized, up to tree-like deformation, by an algebraic object called the signature, tensor of all iterated integrals, of the path. It is clear that all tree-like deformation of the path would not change its topological features. For instance, the number of times a planar loop of finite length winds around a point (not lying on the path) is unaltered if one deforms the path in tree-like ways. Therefore, it should be plausible to extract this topological information out from the signature of the loop since the signature is a complete algebraic invariant. In the chapter 6, we express the winding number of a nice loop (respectively linking number of a pair of nice loops) as a linear functional of the signature of the loop (respectively signatures of the pair of loops).

Stochastic linear programming problems are linear programming problems for which one or more data elements are described by random variables. Two-stage stochastic linear programming problems are problems in which a first stage decision is made before the random variables are observed. A second stage, or recourse decision, which varies with these observations compensates for any deficiencies which result from the earlier decision. Many applications areas including water resources, industrial management, economics and finance lead to two-stage stochastic linear programs with recourse. In this dissertation, two algorithms for solving stochastic linear programming problems with recourse are developed and tested. The first is referred to as Quadratic Stochastic Decomposition (QSD). This algorithm is an enhanced version of the Stochastic Decomposition (SD) algorithm of Higle and Sen (1988). The enhancements were designed to increase the computational efficiency of the SD algorithm by introducing a quadratic proximal term in the master program objective function and altering the manner in which the recourse function approximations are updated. We show that every accumulation point of an easily identifiable subsequence of points generated by the algorithm are optimal solutions to the stochastic program with probability 1. The various combinations of the enhancements are empirically investigated in a computational experiment using operations research problems from the literature. The second algorithm is an SD based algorithm for solving a stochastic linear program in which the recourse problem appears in the constraint set. This algorithm involves the use of an exact penalty function in the master program. We find that under certain conditions every accumulation point of a sequence of points generated by the algorithm is an optimal solution to the recourse constrained stochastic program, with probability 1. This algorithm is tested on several operations research problems.

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published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy

published_or_final_version / Statistics / Master / Master of Philosophy

published_or_final_version / Civil Engineering / Doctoral / Doctor of Philosophy

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