Decision, Operations & Information Technologies Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2761
Browse
3 results
Search Results
Item MODELS AND SOLUTION ALGORITHMS FOR EQUITABLE RESOURCE ALLOCATION IN AIR TRAFFIC FLOW MANAGEMENT(2012) Zhong, Ming; BALL, MICHAEL O; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Population growth and economic development lead to increasing demand for travel and pose mobility challenges on capacity-limited air traffic networks. The U.S. National Airspace System (NAS) has been operated near the capacity, and air traffic congestion is expected to remain as a top concern for the related system operators, passengers and airlines. This dissertation develops a number of model reformulations and efficient solution algorithms to address resource allocation problems in air traffic flow management, while explicitly accounting for equitable objectives in order to encourage further collaborations by different stakeholders. This dissertation first develops a bi-criteria optimization model to offload excess demand from different competing airlines in the congested airspace when the predicted traffic demand is higher than available capacity. Computationally efficient network flow models with side constraints are developed and extensively tested using datasets obtained from the Enhanced Traffic Management System (ETMS) database (now known as the Traffic Flow Management System). Representative Pareto-optimal tradeoff frontiers are consequently generated to allow decision-makers to identify best-compromising solutions based on relative weights and systematical considerations of both efficiency and equity. This dissertation further models and solves an integrated flight re-routing problem on an airspace network. Given a network of airspace sectors with a set of waypoint entries and a set of flights belonging to different air carriers, the optimization model aims to minimize the total flight travel time subject to a set of flight routing equity, operational and safety requirements. A time-dependent network flow programming formulation is proposed with stochastic sector capacities and rerouting equity for each air carrier as side constraints. A Lagrangian relaxation based method is used to dualize these constraints and decompose the original complex problem into a sequence of single flight rerouting/scheduling problems. Finally, within a multi-objective utility maximization framework, the dissertation proposes several practically useful heuristic algorithms for the long-term airport slot assignment problem. Alternative models are constructed to decompose the complex model into a series of hourly assignment sub-problems. A new paired assignment heuristic algorithm is developed to adapt the round robin scheduling principle for improving fairness measures across different airlines. Computational results are presented to show the strength of each proposed modeling approach.Item ROBUST REVENUE MANAGEMENT WITH LIMITED INFORMATION : THEORY AND EXPERIMENTS(2009) Lan, Yingjie; Ball, Michael O; Karaesmen, Itir A; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Revenue management (RM) problems with full probabilistic information are well studied. However, as RM practice spreads to new businesses and industries, there are more and more applications where no or only limited information is available. In that respect, it is highly desirable to develop models and methods that rely on less information, and make fewer assumptions about the underlying uncertainty. On the other hand, a decision maker may not only lack data and accurate forecasting in a new application, but he may have objectives (e.g. guarantees on worst-case profits) other than maximizing the average performance of a system. This dissertation focuses on the multi-fare single resource (leg) RM problem with limited information. We only use lower and upper bounds (i.e. a parameter range), instead of any particular probability distribution or random process to characterize an uncertain parameter. We build models that guarantee a certain performance level under all possible realizations within the given bounds. Our methods are based on the regret criterion, where a decision maker compares his performance to a perfect hindsight (offline) performance. We use competitive analysis of online algorithms to derive optimal static booking control policies that either (i) maximize the competitive ratio (equivalent to minimizing the maximum regret) or (ii) minimize the maximum absolute regret. Under either criterion, we obtain closed-form solutions and investigate the properties of optimal policies. We first investigate the basic multi-fare model for booking control, assuming advance reservations are not cancelled and do not become no-shows. The uncertainty in this problem is in the demand for each fare class. We use information on lower and upper bounds of demand for each fare class. We determine optimal static booking policies whose booking limits remain constant throughout the whole booking horizon. We also show how dynamic policies, by adjusting the booking limits at any time based on the bookings already on hand, can be obtained. Then, we integrate overbooking decisions to the basic model. We consider two different models for overbooking. The first one uses limited information on no-shows; again the information being the lower and upper bound on the no-show rate. This is appropriate for situations where there is not enough historical data, e.g. in a new business. The second model differs from the first by assuming the no-show process can be fully characterized with a probabilistic model. If a decision-maker has uncensored historical data, which is often the case in reality, he/she can accurately estimate the probability distribution of no-shows. The overbooking and booking control decisions are made simultaneously in both extended models. We derive static overbooking and booking limits policies in either case. Extensive computational experiments show that the proposed methods that use limited information are very effective and provide consistent and robust results. We also show that the policies produced by our models can be used in combination with traditional ones to enhance the system performance.Item Empirical Analyses on Federal Thrift Savings Plan Portfolio Optimization(2007-11-27) Nestler, Scott T; Fu, Michael C; Madan, Dilip B; Business and Management: Decision & Information Technologies; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)There is ample historical data to suggest that log returns of stocks and indices are not independent and identically distributed Normally, as is commonly assumed. Instead, the returns of financial assets are skewed and have higher kurtosis. To account for skewness and excess kurtosis, it is necessary to have a distribution that is more flexible than the Gaussian distribution and uses additional information that may be present in higher moments. The federal government's Thrift Savings Plan (TSP) is the largest defined contribution retirement savings and investment plan, with nearly 3.6 million participants and over $173 billion in assets. The TSP offers five assets (government bond fund, fixed income fund, large-cap stock fund, small-cap stock fund, and international stock fund) to U.S. government civilian employees and uniformed service members. The limited choice of investments, in comparison to most 401(k) plans, may be disappointing from a participant's perspective; however, it provides an attractive framework for empirical study. In this study, we investigate how the optimal choice of TSP assets changes when traditional portfolio optimization methods are replaced with newer techniques. Specifically, the following research questions are posed and answered: (1) Does use of a non-Gaussian factor model for returns, generated with independent components analysis (ICA) and following the Variance Gamma (VG) process, provide any advantage in constructing optimal TSP portfolios? (2) Can excess TSP portfolio returns be generated through rebalancing to an optimal mix? If so, what frequency of rebalancing provides benefits that offset increased computationalal and administrative burden? (3) How does the use of coherent risk and portfolio performance measures, in place of variance as the traditional the measure for risk and Sharpe Ratio as the usual portfolio performance measure affect TSP portfolio selection? We show through simulation that some of the newer schemes should produce excess returns over conventional (mean-variance optimization with Normally-distributed returns) portfolio choice models. The use of some or all of these methods could benefit the nearly 4 million TSP participants in achieving their retirement savings and investment objectives. Furthermore, we propose two new portfolio performance measures based on recent developments in coherent measures of risk.