Wildfires have become more destructive and are seriously threatening societies and our ecosystems throughout the world. Once a wildfire escapes from its initial suppression attack, it can easily develop into a destructive huge fire that can result in significant loss of lives and resources. Some human-caused wildfires may be prevented; however, most nature-caused wildfires cannot. Consequently, wildfire suppression and contain- ment becomes fundamentally important; but suppressing and containing wildfires is costly. Since the budget and resources for wildfire management are constrained in reality, it is imperative to make important decisions such that the total cost and damage associated with the wildfire is minimized while wildfire containment effectiveness is maximized. To achieve this objective, wildfire attack-bases should be optimally located such that any wildfire is suppressed within the effective attack range from some bases. In addition, the optimal fire-fighting resources should be deployed to the wildfire location such that it is efficiently suppressed from an economic perspective. The two main uncertain/stochastic factors in wildfire management problems are fire occurrence frequency and fire growth characteristics. In this thesis two models for wildfire management planning are proposed. The first model is a strategic model for the optimal location of wildfire-attack bases under uncertainty in fire occurrence. The second model is a tactical model for the optimal deployment of fire-fighting resources under uncertainty in fire growth. A stochastic mixed-integer programming approach is proposed in order to take into account the uncertainty in the problem data and to allow for robust wildfire management decisions under uncertainty. For computational results, the tactical decision model is numerically experimented by two different approaches to provide the more efficient method for solving the model.

Programming teaching is not effective from experience. Research shows that the main problem novice programmers have is not understanding the syntax and semantics of programming language, but combining the knowledge they have learned to make a solution of a new problem. Patterns are useful to combine the knowledge and develop a solution for a problem. A lot of research suggests that using patterns in teaching introductory programming courses, but most of them just define the patterns and give some examples of them. However, just a few of them use programming patterns in a real course or make patterns into the teaching materials. This research proposes a programming pattern tutorial for elementary novice students and adopts the active research method to teach the material in a programming course of the first grade students of NSYSU. We collect and analyze the data coming from participating observation, interview record, and the test scripts of midterm and final term exams and hope to use action research method to categorize the difficulties we have encountered during the teaching process. In conclusion, the programming pattern material this research proposes can actually help students improve the ability of solving problems. In addition, they know how to start to start to solve a problem, have the algorithm idea in their mind and search solutions they have made to similar problems. Finally, write a solution to a new problem.

Branch-price-and-cut algorithms are among the most successful exact optimization approaches for solving many routing and scheduling problems. This is due, in part, to the availability of extremely efficient and effective dynamic programming algorithms for solving the pricing problem, and the availability of efficient and effective branching schemes and cutting planes that drive integrality. In terms of branch-price-and-cut, two obstacles we face today are (1) being able to solve harder and larger pricing problems, and (2) solving mixed-integer column generation formulations that suffer from relatively weak LP bounds compared to the more traditional 0-1 set partitioning type. As part of the work presented in this thesis, we encounter column generation formulations motivated by real life problems that require overcoming both types of challenges. The first part of this thesis is dedicated to solving the resource constrained shortest path problem (RCSPP) arising in column generation pricing problems for formulations involving extremely large networks and a huge number of local resource constraints. We present a relaxation-based dynamic programming algorithm that alternates between a forward and a backward search. Each search employs bounds derived in the previous search to prune the search, and between consecutive searches, the relaxation is tightened over a set of critical resources and arcs. The second part of this thesis focuses in the fixed charge shortest path problem (FCSPP) in which the amount of resource consumed is itself a continuous bounded variable. By exploiting the structure of optimal solutions to FCSPP, we design and implement a solution approach that relies on solving multiple RCSPPs, and therefore can again make use of extremely efficient and effective dynamic programming algorithms. In the third and final part of this thesis, we present a branch-price-and-cut algorithm for the inventory routing problem (IRP). We extend a class of cuts known for the vehicle routing problem, and develop a new class of cuts specifically for IRP to tighten the formulation. Both the branching schemes and cuts preserve the structure of the pricing problem making them efficiently implementable within a branch-price-and-cut algorithm.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 199-201). Also available on the Internet.

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): David Olwell, Samuel E. Buttrey. Includes bibliographical references (p. 79). Also available online.

Thesis (Honors)--College of William and Mary, 2009. / Includes bibliographical references (leaf 37). Also available via the World Wide Web.

Thesis (M.S.)--West Virginia University, 2002. / Title from document title page. Document formatted into pages; contains vii, 76 p. : ill. Includes abstract. Includes bibliographical references (p. 72-76).

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Robert A. Koyak. Includes bibliographical references (p. 59-61). Also available online.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, March 2004. / Thesis advisor(s): R. Kevin Wood, Moshe Kress, Gerald G. Brown. Includes bibliographical references (p. 47-48). Also available online.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (p. 96-105).