http://hdl.handle.net/1721.1/7720 http://hdl.handle.net/1721.1/47567 Design of large scale transportation service networks with consolidation : models, algorithms and applications Krishnan, Niranjan, 1973- Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 1998. Includes bibliographical references (leaves 94-103). Wed, 29 Oct 1997 22:58:59 GMT http://hdl.handle.net/1721.1/45952 Managing portfolios of products and securities Quinteros, Martin In this thesis we study modifications of the classical Mean-Variance Portfolio Optimization model. Our objective is to identify an optimal subset of assets from all available assets to maximize the expected return while incurring the minimum risk. In addition, we test several approaches to measuring the effect of the variance of the portfolio on the optimal asset allocation. We have developed a mixed integer formulation to solve the well known Markowitz portfolio model. Our model captures and solves the certain practical drawbacks that a real investor would face with the Markowitz approach. For example, by selecting a limited number of assets our procedure tends to prevent small allocations of assets. In addition, we find that in most cases, the maximum drawdown increases as a function of the upper bound on the variance of the portfolio and that this result is consistent with intuition, since portfolio risk increases as the chance that a drawdown event occurs also increases. However, we have observed that altering the composition of the portfolio can mitigate the risk of a drawdown event. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. Includes bibliographical references (leaf 91). Mon, 29 Oct 2007 22:58:59 GMT http://hdl.handle.net/1721.1/45801 Optimized supply routing at Dell under non-stationary demand Foreman, John William This thesis describes the design and implementation of an optimization model to manage inventory at Dell's American factories. Specifically, the model is a mixed integer program which makes routing decisions on incoming monitors (a bulky item which incurs great shipping costs) from Asia to Dell's factories in America as well as inventory transfer decisions from factory to factory. The optimization model approaches the inventory allocation problem by minimizing inventory routing costs plus shortage costs across all sites subject to constraints which define the specifics of Dell's supply chain. Shortage costs are assessed using a per part per day back order penalty, however a more precise assessment of shortage costs using actual costs from a combined MIT/Dell study is also presented. The software implementation of the optimization model has been field tested and validated and is now being adopted on a global level for use in balancing supply to all of Dell's factories worldwide. The software design as well as the implementation results are discussed within this thesis. Also, an adaptation of the model to a global scale is presented. This extension of the model, which assumes a "global warehouse" upstream in the supply chain, allocates inventory from the China to regional facilities throughout the world subject to supply chain constraints and the understanding that regional teams will tend to balance out their own region's inventory using intraregional balancing decisions. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. Includes bibliographical references (p. 79-80). Mon, 29 Oct 2007 22:58:59 GMT http://hdl.handle.net/1721.1/45800 Estimation of sell-up potential in airline revenue management systems Guo, Jingqiang Charles The growth of Low Fare Carriers (LFCs) has encouraged many airlines to remove fare restrictions (such as advance purchase requirements and Saturday-night stays) on many of their fare class products, leading to the simplification of fare structures in competitive markets. In the most extreme case, these markets have fare structures that are unrestricted; the fare class products differ only by price since they AL1 lack restrictions. In these unrestricted markets, passengers buy the lowest possible fare product since there are no longer any restrictions that prevent them from doing so. A forecasting method known as "Q-forecasting" takes into account the sell- up potential of passengers in forecasting the demand in each of the fare products in such markets. Sell-up occurs when passengers upon being denied their original fare class choice, decide to pay more for the next available fare class so long as the price remains below their maximum willingness to pay. Quantifying this sell-up potential either using estimated or input values is thus crucial in helping airlines increase revenues when competing in unrestricted fare markets. A simulation model known as the Passenger Origin-Destination Simulator (PODS) contains the following 3 sell-up estimation methods: (i) Direct Observation (DO), (ii) Forecast Prediction (FP), and (iii) Inverse Cumulative (IC). The goal of this thesis is thus to investigate and compare the revenue performance of the 3 sell-up estimation methods. These methods are tested in a 2-airline (consisting of AL1 and AL2) unrestricted network under different RM fare class optimization scenarios. (cont.) Both estimated and input sell-up values are tested on AL1 whereas only input sell-up values are tested on AL2. The findings of the simulations indicate that using FP typically results in the highest revenues for AL1 among AL1 3 sell-up estimation methods. When compared against simple RM fare class threshold methods that do not consider sell-up, using FP results in up to a 3% revenue gain for AL1. Under some fare class optimization scenarios, using FP instead of input sell-up values even results in a revenue increase of close to 1%. These findings suggest that FP is robust enough under a range of fare class optimizers to be used by airlines as a sell-up estimator in unrestricted fare environments so as to raise revenues. Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. Includes bibliographical references (p. 69-71). Mon, 29 Oct 2007 22:58:59 GMT