Two-Stage Stochastic Mixed Integer Nonlinear Programming: Theory, Algorithms, and Applications
With the rapidly growing need for long-term decision making in the presence of stochastic future events, it is important to devise novel mathematical optimization tools and develop computationally efficient solution approaches for solving them. Two-stage stochastic programming is one of the powerful modeling tools that allows probabilistic data parameters in mixed integer programming, a well-known tool for optimization modeling with deterministic input data. However, akin to the mixed integer programs, these stochastic models are theoretically intractable and computationally challenging to solve because of the presence of integer variables. This dissertation focuses on theory, algorithms and applications of two-stage stochastic mixed integer (non)linear programs and it has three-pronged plan. In the first direction, we study two-stage stochastic p-order conic mixed integer programs (TSS-CMIPs) with p-order conic terms in the second-stage objectives. We develop so called scenario-based (non)linear cuts which are added to the deterministic equivalent of TSS-CMIPs (a large-scale deterministic conic mixed integer program). We provide conditions under which these cuts are sufficient to relax the integrality restrictions on the second-stage integer variables without impacting the integrality of the optimal solution of the TSS-CMIP. We also introduce a multi-module capacitated stochastic facility location problem and TSS-CMIPs with structured CMIPs in the second stage to demonstrate the significance of the foregoing results for solving these problems. In the second direction, we propose risk-neutral and risk-averse two-stage stochastic mixed integer linear programs for load shed recovery with uncertain renewable generation and demand. The models are implemented using a scenario-based approach where the objective is to maximize load shed recovery in the bulk transmission network by switching transmission lines and performing other corrective actions (e.g. generator re-dispatch) after the topology is modified. Experiments highlight how the proposed approach can serve as an offline contingency analysis tool, and how this method aids self-healing by recovering more load shedding. In the third direction, we develop a dual decomposition approach for solving two-stage stochastic quadratically constrained quadratic mixed integer programs. We also create a new module for an open-source package DSP (Decomposition for Structured Programming) to solve this problem. We evaluate the effectiveness of this module and our approach by solving a stochastic quadratic facility location problem.