The U.S. Navy and other amphibious military organizations utilize a derivation of the traditional side stroke called the Combat Side Stroke, or CSS, and tout it as the most efficient technique available. Citing its low aerobic requirements and slow yet powerful movements as superior to the traditionally-best front crawl (freestyle), the CSS is the go-to stroke for any operation in the water. The purpose of this thesis is to apply principles of Industrial Engineering to a real-world situation not typically approached from a perspective of optimization. I will analyze pre-existing data about various swim strokes in order to compare them in terms of efficiency for different variables. These variables include calories burned, speed, and strokes per unit distance, as well as their interactions. Calories will be measured by heart rate monitors, converting BPM to calories burned. Speed will be measured by stopwatch and observer. Strokes per unit distance will be measured by observer. The strokes to be analyzed include the breast stroke, crawl stroke, butterfly, and combat side stroke. The goal is to informally test the U.S. Navy's claim that the combat side stroke is the optimum stroke to conserve energy while covering distance. Because of limitations in the scope of the project, analysis will be done using data collected from literary sources rather than through experimentation. This thesis will include a design of experiment to test the findings here in practical study. The main method of analysis will be linear programming, followed by hypothesis testing, culminating in a design of experiment for future progress on this topic.
The first model offers an alternative formulation to the traditional choice-based revenue management problem presented in the literature, and provides substantial gains in expected revenue while limiting the problem’s computational complexity. Making assumptions on passenger demand, the Choice-based Mixed Integer Program (CMIP) provides a significantly more compact formulation when compared to other choice-based revenue management models, and consistently outperforms previous models.
Despite the prevalence of choice-based revenue management models in literature, the assumptions made on purchasing behavior inhibit researchers to create models that properly reflect passenger sensitivities to various ticket attributes, such as price, number of stops, and flexibility options. This dissertation introduces a general framework for airline choice-based demand modeling that takes into account various ticket attributes in addition to price, providing a framework for revenue management models to relate airline companies’ product design strategies to the practice of revenue management through decisions on ticket availability and price.
Finally, this dissertation introduces a mixed integer non-linear programming formulation for airline revenue management that accommodates the possibility of simultaneously setting prices and availabilities on a network. Traditional revenue management models primarily focus on availability, only, forcing secondary models to optimize prices. The Price-dynamic Choice-based Mixed Integer Program (PCMIP) eliminates this two-step process, aligning passenger purchase behavior with revenue management policies, and is shown to outperform previously developed models, providing a new frontier of research in airline revenue management.