Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Integer programs (IPs) are a commonly researched class of decision problems. These problems are used in various applications to help companies, governments, or individuals make better decisions by determining optimal resource allocations. While IPs are practical tools, they require an exponential amount of effort to solve, unless P = NP. This fact has led to much research focused on reducing the time required to solve IPs. Cutting planes are a commonly used tool for reducing IP solving time. Lifting, a process of changing the coefficients in an inequality, is often employed to strengthen cutting planes. When lifting, the goal is often to create a facet defining inequality, which is theoretically the strongest cutting plane. This thesis introduces two new lifting procedures for the Node Packing problem. The Node Packing problem seeks to select the maximum number of nodes in a graph such that no two nodes are adjacent. The first lifting method, the Simultaneous Lifting Expansion, takes two inequalities and combines them to make a stronger cut. It works for any two general classes of inequalities, as long as the requisite graph structures are met. The second method, the Cliques On Odd-holes Lifting (COOL) procedure, lifts from an odd-hole inequality to a facet defining inequality. COOL makes use of the Odd Gap Lifting procedure, an efficient method for finding lifting coefficients on odd holes. A computational study shows COOL to be effective in creating cuts in graphs with low edge densities.

Master of Science / Department of Industrial and Manufacturing Systems Engineering / Todd Easton / Graph theory is a widely researched topic. A graph contains a set of nodes and a set of edges. The nodes often represent resources such as machines, employees, or plant locations. Each edge represents the relationship between a pair of nodes such as time, distance, or cost. Integer programs are frequently used to solve graphical problems. Unfortunately, IPs are NP-hard unless P = NP, which implies that it requires exponential effort to solve them. Much research has been focused on reducing the amount of time required to solve IPs through the use of valid inequalities or cutting planes. The theoretically strongest cutting planes are facet defining cutting planes. This research focuses on the node packing problem or independent set problem, which is a combinatorial optimization problem. The node packing problem involves coloring the maximum number of nodes such that no two nodes are adjacent. Node packings have been applied to airline traffic and radio frequencies. This thesis introduces a new class of graphical structures called suns. Suns produce previously undiscovered valid inequalities for the node packing polyhedron. Conditions are provided for when these valid inequalities are proven to be facet defining. Sun valid inequalities have the potential to more quickly solve node packing problems and could even be extended to general integer programs through conflict graphs.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / Graph Theory is a widely studied topic. A graph is defined by two important features: nodes and edges. Nodes can represent people, cities, variables, resources, products, while the edges represent a relationship between two nodes. Using graphs to solve problems has played a major role in a diverse set of industries for many years. Integer Programs (IPs) are mathematical models used to optimize a problem. Often this involves maximizing the utilization of resources or minimizing waste. IPs are most notably used when resources must be of integer value, or cannot be split. IPs have been utilized by many companies for resource distribution, scheduling, and conflict management. The node packing or independent set problem is a common combinatorial optimization problem. The objective is to select the maximum nodes in a graph such that no two nodes are adjacent. Node packing has been used in a wide variety of problems, which include routing of vehicles and scheduling machines. This thesis introduces several new graph structures, cliqued hole, odd bipartite hole, and odd k-partite hole, and their corresponding valid inequalities for the node packing polyhedron. These valid inequalities are shown to be new valid inequalities and conditions are provided for when they are facet defining, which are known to be the strongest class of valid inequalities. These new valid inequalities can be used by practitioners to help solve node packing instances and integer programs.

With recent and continuing advances in bioinformatics, the volume of sequence data has increased tremendously. Along with this increase, there is a growing need to develop efficient algorithms to process such data in order to make useful and important discoveries. Careful analysis of genomic data will benefit science and society in numerous ways, including the understanding of protein sequence functions, early detection of diseases, and finding evolutionary relationships that exist among various organisms. Most sequence analysis problems arising from computational genomics and evolutionary biology fall into the class of NP-complete problems. Advances in exact and approximate algorithms to address these problems are critical. In this thesis, we investigate a novel graph theoretical model that deals with fundamental evolutionary problems. The model allows incorporation of the evolutionary operations ``insertion', ``deletion', and ``substitution', and various parameters such as relative distances and weights. By varying appropriate parameters and weights within the model, several important combinatorial problems can be represented, including the weighted supersequence, weighted superstring, and weighted longest common sequence problems. Consequently, our model provides a general computational framework for solving a wide variety of important and difficult biological sequencing problems, including the multiple sequence alignment problem, and the problem of finding an evolutionary ancestor of multiple sequences. In this thesis, we develop large scale combinatorial optimization techniques to solve our graph theoretical model. In particular, we formulate the problem as two distinct but related models: constrained network flow problem and weighted node packing problem. The integer programming models are solved in a branch and bound setting using simultaneous column and row generation. The methodology developed will also be useful to solve large scale integer programming problems arising in other areas such as transportation and logistics.

著色問題(graph coloring problem)的研究已行之有年，並衍生出廣泛的實際應用，但還缺乏一般化的著色問題模型。本論文建構一般化的著色問題模型，其目標函數包含顏色成本的固定支出和點著色變動成本。此著色模型為0/1整數線性規畫模型，其限制式含有選點問題(node packing problem)的限制式。我們利用圖中的極大團(maximal clique)所構成的強力限制式，取代原有的選點限制式，縮短求解時間。我們更進一步舉出一個特殊指派問題並將此著色模型應用於此指派問題上。本論文亦針對此指派問題發展了一個演算法來尋找極大團。計算結果顯示極大團限制式對於此著色問題模型的求解有極大的效益。 / The graph coloring problem (GCP) has been studied for a long time and it has a wide variety of applications. A straightforward formulation of graph coloring problem has not been formulated yet. In this paper, we formulate a general GCP model that concerns setup cost and variable cost of different colors. The resulting model is an integer program that involves the packing constraint. The packing constraint in the GCP model can be replaced by the maximal clique constraint in order to shorten the solution time. A special assignment problem is presented which essentially is a GCP model application. An algorithm of finding maximal cliques for this assignment problem is developed. The computational results show the efficiency of maximal clique constraints for the GCP problem.