http://hdl.handle.net/1721.1/39113 http://hdl.handle.net/1721.1/45759 Blast mitigation strategies for vehicles using shape optimization methods Gurumurthy, Ganesh Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. Includes bibliographical references (p. 69-72). http://hdl.handle.net/1721.1/45287 A comparison of discrete and flow-based models for air traffic flow management Phu, Thi Vu The steady increase of congestion in air traffic networks has resulted in significant economic losses and potential safety issues in the air transportation. A potential way to reduce congestion is to adopt efficient air traffic management policies, such as, optimally scheduling and routing air traffic throughout the network. In recent years, several models have been proposed to predict and manage air traffic. This thesis focuses on the comparison of two such approaches to air traffic flow management: (i) a discrete Mixed Integer Program model, and (ii) a continuous flow-based model. The continuous model is applied in a multi-commodity setting to take into account the origins and destinations of the aircraft. Sequential quadratic programming is used to optimize the continuous model. A comparison of the performance of the two models based on a set of large scale test cases is provided. Preliminary results suggest that the linear programming relaxation of the discrete model provides results similar to the continuous flow-based model for high volumes of air traffic. Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. Includes bibliographical references (leaves 73-74). http://hdl.handle.net/1721.1/45286 Empirical comparison of robust, data driven and stochastic optimization Wang, Yanbo, S.M. Massachusetts Institute of Technology In this thesis, we compare computationally four methods for solving optimization problems under uncertainty: * Robust Optimization (RO) * Adaptive Robust Optimization (ARO) * Data Driven Optimization (DDO) * stochastic Programming (SP) We have implemented several computation experiments to demonstrate the different performance of these methods. We conclude that ARO outperform RO, which has a comparable performance with DDO. SP has a comparable performance with RO when the assumed distribution is the same as the true underlying distribution, but under performs RO when the assumed distribution is different from the true distribution. Includes bibliographical references (leaf 49). Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. http://hdl.handle.net/1721.1/45285 Optimal operating strategy for a storage facility Zhai, Ning In the thesis, I derive the optimal operating strategy to maximize the value of a storage facility by exploiting the properties in the underlying natural gas spot price. To achieve the objective, I investigate the optimal operating strategy under three different spot price processes: the one-factor mean reversion price process with and without seasonal factors, the one-factor geometric Brownian motion price process with and without seasonal factors, and the two-factor short-term/long-term price process with and without seasonal factors. I prove the existence of the unique optimal trigger prices, and calculate the trigger prices under certain conditions. I also show the optimal trigger prices are the prices where the marginal revenue is equal to the marginal cost. Thus, the marginal analysis argument can be used to determine the optimal operating strategy. Once the optimal operating strategy is determined, I use it to obtain the optimal value of the storage facility in three ways: 1, using directly the net present value method; 2, solving the partial differential equations governing the value of the storage facility; 3, using the Monte Carlo method to simulate the decision making process. Issues about parameter estimations are also considered in the thesis. Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. Includes bibliographical references (p. 100-101).