Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2002. / Includes bibliographical references (p. 59-60). / We develop two classes of optimization models in order to maximize revenue in a restaurant, while controlling average waiting time as well as perceived fairness, that may violate the first-come-first-serve (FCFS) rule. In the first class of models, we use integer programming, stochastic programming and approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation of a restaurant that does not accept reservations. In a computational study with simulated data, we show that optimization based methods enhance revenle relative to the industry practice of FCFS by 0.11% to 2.22% for low load factors, by 0.16% to 2.96% for medium load factors, and by 7.65% to 13.13% for high load factors, without increasing and occasionally decreasing waiting times compared to FCFS. The second class of models addresses reservations. We propose a two step procedure: use a stochastic gradient algorithm to decide a priori how many reservations to accept for a future time and then use approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation. In a computational study involving real data from an Atlanta restaurant, the reservation model improves revenue relative to FCFS by 3.5% for low load factors and 7.3% for high load factors. / by Romy Shioda. / S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 193-201). / The global airline industry is a multi-stakeholder stochastic system whose performance is the outcome of complex interactions between its multiple decisions-makers under a high degree of uncertainty. Inadequate understanding of uncertainty and stakeholder preferences leads to adverse effects including airline losses, delays and disruptions. This thesis studies a set of topics in airline scheduling and air traffic control to mitigate some of these issues. The first part of the thesis focuses on building aircraft schedules that are robust against delays. We develop a robust optimization approach for building aircraft routes. The goal is to mitigate propagated delays, which are defined as the delays caused by the late arrival of aircraft from earlier flights and are the top cause of flight delays in the United States air transportation system. The key feature of our model is that it allows us to handle correlation in flight delays explicitly that existing approaches cannot handle efficiently. We propose an efficient decomposition algorithm to solve the robust model and present the results of numerical experiments, based on data from a major U.S. airline, to demonstrate its effectiveness compared to existing approaches. The second part of the thesis focuses on improving the planning of air traffic flow management (ATFM) programs by incorporating airline preferences into the decision-making process. We develop a voting mechanism to gather airline preferences of candidate ATFM designs. A unique feature of this mechanism is that the candidates are drawn from a domain with infinite cardinality described by polyhedral sets. We conduct a detailed case study based on actual schedule data at San Francisco International Airport to assess its benefits in planning of ground delay programs. Finally, we study an integrated airline network planning model which incorporates passenger choice behavior. We model passenger demand using a multinomial logit choice model and integrate it into a fleet assignment and schedule design model. To tackle the formidable computational challenge associated with solving this model, we develop a reformulation, decomposition and approximation scheme. Using data from a major U.S. airline, we prove that the proposed approach brings significant profit improvements over existing methods. / by Chiwei Yan. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 163-166). / In this thesis, we study data-driven decision making in operation management contexts, with a focus on both theoretical and practical aspects. The first part of the thesis analyzes the well-known newsvendor model but under the assumption that, even though demand is stochastic, its probability distribution is not part of the input. Instead, the only information available is a set of independent samples drawn from the demand distribution. We analyze the well-known sample average approximation (SAA) approach, and obtain new tight analytical bounds on the accuracy of the SAA solution. Unlike previous work, these bounds match the empirical performance of SAA observed in extensive computational experiments. Our analysis reveals that a distribution's weighted mean spread (WMS) impacts SAA accuracy. Furthermore, we are able to derive distribution parametric free bound on SAA accuracy for log-concave distributions through an innovative optimization-based analysis which minimizes WMS over the distribution family. In the second part of the thesis, we use spread information to introduce new families of demand distributions under the minimax regret framework. We propose order policies that require only a distribution's mean and spread information. These policies have several attractive properties. First, they take the form of simple closed-form expressions. Second, we can quantify an upper bound on the resulting regret. Third, under an environment of high profit margins, they are provably near-optimal under mild technical assumptions on the failure rate of the demand distribution. And finally, the information that they require is easy to estimate with data. We show in extensive numerical simulations that when profit margins are high, even if the information in our policy is estimated from (sometimes few) samples, they often manage to capture at least 99% of the optimal expected profit. The third part of the thesis describes both applied and analytical work in collaboration with a large multi-state gas utility. We address a major operational resource allocation problem in which some of the jobs are scheduled and known in advance, and some are unpredictable and have to be addressed as they appear. We employ a novel decomposition approach that solves the problem in two phases. The first is a job scheduling phase, where regular jobs are scheduled over a time horizon. The second is a crew assignment phase, which assigns jobs to maintenance crews under a stochastic number of future emergencies. We propose heuristics for both phases using linear programming relaxation and list scheduling. Using our models, we develop a decision support tool for the utility which is currently being piloted in one of the company's sites. Based on the utility's data, we project that the tool will result in 55% reduction in overtime hours. / by Joline Ann Villaranda Uichanco. / Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 243-250). / In this thesis, I study how data-driven optimization can be used to improve school choice. In a typical school choice system, each student receives a set of school options, called the student's menu. Based on his/her menu, each student submits a preference ranking of schools in the menu. Based on the submitted preferences, a centralized algorithm determines the assignment. In Boston, New York City, Chicago, Denver, New Orleans,Washington DC, among other cities, the assignment algorithm is the student-proposing deferred acceptance (DA) algorithm, which can also incorporate a priority for each student at each school. These priorities may contain a deterministic as well as a random component. An advantage of this algorithm is incentive-compatibility, meaning that no student has incentives to misreport his/her preferences. The first research question of this thesis is how to optimize the menus and priorities so that students have equitable chances to go to the schools they want, while the city's school busing costs are controlled. The second question is how the assignment algorithm can be modified to keep the same assignment probability of every student to every school, while improving neighbors' chances of going to the same school. To answer these questions, I build a multinomial logit (MNL) model to predict how students will rank schools under new menus, and validate the predictive accuracy of this model out of sample. I also propose a simple plan for menus and priorities, called the Home-Based plan, and compare with other proposals using the MNL model. (As a result of this analysis, the Home-Based plan was adopted by Boston in 2013.) I then show how one can further optimize the menus and priorities under the MNL model, by developing a new theoretical connection between stable matching and assortment planning, as well as methodologies on solving a new type of assortment planning problem, in which the objective is social welfare rather than revenue. Finally, I show how to further optimize the correlations between students' assignments to improve neighbors' chances of going to the same school. / by Peng Shi. / Ph. D.

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 95-97). / End-stage liver disease is one of the leading causes of death in the United States, and the only viable treatment is liver transplantation. Since the quality of a donor liver decreases with transportation time, United States organ policy prioritizes transplants within geographic regions. However, the boundaries of these regions were defined mostly by informal relationships between transplant centers many decades ago, which has created local imbalances in supply and demand. As a result, candidates on the waiting list for donor livers face drastically different odds of receiving a transplant. Policy makers have noticed this geographic inequity and are considering proposals for alternative liver allocation approaches. This thesis uses mathematical optimization to redesign liver allocation regions by modeling and including several key elements of the allocation process directly in the optimization formulation. Specifically, we use a fluid approximation to model the dynamics of the wait-list progression and liver allocation. The model is fit using historical data of wait-list candidates and donors. Then, we propose two optimization formulations to reduce geographic inequality. The first directly minimizes the variation in median level of illness at the time of transplant across geographical areas, which is a key metric used by policy makers in addressing geographic inequality. The second approach minimizes the liver transport distance, subject to a certain allowable level of geographic variation. We discuss how these models can flexibly incorporate additional policy constraints to create more realistic models to reduce geographic variation. The region configurations are evaluated on key metrics relating to fairness and system efficiency using a standardized, validated, simulation approach widely accepted by policymakers. Finally, we propose a region design that significantly reduces geographic inequality without any substantial impact on the system's efficiency. / by Timothy Scully. / S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 201-207). / Modern probability theory, whose foundation is based on the axioms set forth by Kolmogorov, is currently the major tool for performance analysis in stochastic systems. While it offers insights in understanding such systems, probability theory, in contrast to optimization, has not been developed with computational tractability as an objective when the dimension increases. Correspondingly, some of its major areas of application remain unsolved when the underlying systems become multidimensional: Queueing networks, auction design in multi-item, multi-bidder auctions, network information theory, pricing multi-dimensional financial contracts, among others. We propose a new approach to analyze stochastic systems based on robust optimization. The key idea is to replace the Kolmogorov axioms and the concept of random variables as primitives of probability theory, with uncertainty sets that are derived from some of the asymptotic implications of probability theory like the central limit theorem. In addition, we observe that several desired system properties such as incentive compatibility and individual rationality in auction design and correct decoding in information theory are naturally expressed in the language of robust optimization. In this way, the performance analysis questions become highly structured optimization problems (linear, semidefinite, mixed integer) for which there exist efficient, practical algorithms that are capable of solving problems in high dimensions. We demonstrate that the proposed approach achieves computationally tractable methods for (a) analyzing queueing networks (Chapter 2) (b) designing multi-item, multi-bidder auctions with budget constraints, (Chapter 3) (c) characterizing the capacity region and designing optimal coding and decoding methods in multi-sender, multi-receiver communication channels (Chapter 4). / by Chaithanya Bandi. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2003. / Includes bibliographical references (p. 160-168). / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / We propose new models and optimization methods for airline revenue management and pricing. In the first part of this thesis, we study the dynamic inventory control problem for a single flight under imperfect market segmentation, when customers book the lowest available class whose restrictions they can satisfy and whose fare they are willing to pay. We derive theoretical properties of the value functions and optimal policy for a generic single-resource revenue management problem, of which this problem is a special case. Numerical examples show that adjusting the booking policy for imperfections of the market segmentation leads to significant revenue gains. In the second part, we study the impact of dynamic capacity management on airline seat inventory control. Through better matching the supply and demand for seats the airline is able to carry more passengers, and the revenue management policy should be adjusted accordingly. We propose a derivative of the widely used EMSRb booking limit calculation method that takes into account the effect of future capacity changes, which can lead to significant revenue gains. In the third part, we propose a simulation-based optimization approach for seat inventory control in a network environment. Starting with any nested booking-limit policy, we combine a stochastic gradient algorithm and approximate dynamic programming ideas to improve the initial booking limits. Numerical experiments suggest that the proposed algorithm can lead to practically significant revenue enhancements. In the fourth part, -we study a joint pricing and resource allocation probleml in a network with applications to production planning and airline revenue management. We show that the objective function reduces to a convex optimization problem for certain types of demand distributions, which is tractable for large instances. / (cont.) We propose several approaches for dynamic picing and resource allocation. Numerical experiments suggest that coordination of pricing and resource allocation policies in a network while taking into account the uncertainty of demand can lead to significant revenue gains. Finally, in our conclusions we propose an integrated approach to airline revenue management that combines all four aspects that we studied here, and suggest directions for future research. / by Sanne Vincent de Boer. / Ph.D.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 198-204). / The problem of investing over time remains an important open question, considering the recent large moves in the markets, such as the Financial Crisis of 2008, the subsequent rally in equities, and the decline in commodities over the past two years. We study this problem from three aspects. The first aspect lies in analyzing a particular dynamic strategy, called the stop-loss strategy. We derive closed-form expressions for the strategy returns while accounting for serial correlation and transactions costs. When applied to a large sample of individual U.S. stocks, we show that tight stop-loss strategies tend to underperform the buy-and- hold policy due to excessive trading costs. Outperformance is possible for stocks with sufficiently high serial correlation in returns. Certain strategies succeed at reducing downside risk, but not substantially. We also look at optimizing the stop-loss level for a class of these strategies. The second approach is more behavioral in nature and aims to elicit how various market players expect to react to large changes in asset prices. We use a global survey of individual investors, financial advisors, and institutional investors to do this. We find that most institutional investors expect to exhibit highly contrarian reactions to past returns in terms of their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. In contrast, individual investors are, on average, extrapolative, and can be partitioned into four distinct types: passive investors, risk avoiders, extrapolators, and everyone else. The third part of the thesis studies how people actually trade. We propose a new model of dynamic trading in which an investor is affected by behavioral heuristics, and carry out extensive simulations to understand how the heuristics affect portfolio performance. We propose an MCMC algorithm that is reasonably successful at estimating model parameters from simulated data, and look at the predictive ability of the model. We also provide preliminary results from looking at trading data obtained from a brokerage firm. We focus on understanding how people trade their portfolios conditional on past returns at various horizons, as well as on past trading behavior. / by Alexander Remorov. / Ph. D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004. / Includes bibliographical references. / Theorists suggest the rising value of human capital will mean greater participation by employees in the ownership and governance of firms. This thesis explores aspects of these claims by analyzing the competitive effects of the allocation of ownership and authority in ad agencies and airlines. Essays 1 and 2 analyze the organizational history of the advertising industry to reconcile the stylized, theoretical views of professional service firms (PSFs) as unstable, small, private partnerships with the empirical reality of large public corporations in several professional service industries. Essay 1 uses a panel of advertising agencies and creativity awards from 1960-1980 to assess whether public ownership reduced PSF competitiveness, particularly whether it diluted employee incentives. Finding no difference in the survival, growth, and award rates of public and private ad agencies, this paper challenges the notion that allocating ownership exclusively to employees provides advantage in the PSF environment. Essay 2 draws on interviews and historical research to develop hypotheses about the structure and evolution of the industry. It proposes that agency size affects the ability to service large projects, hence the size distribution of agencies stems from heterogeneity in the units of demand. It also proposes that the industry's holding companies add value through financial intermediation. Together these essays suggest that the large public corporation is a feasible and perhaps advantaged governance form even in environments based predominantly on human capital. / (cont.) They challenge several assumptions underlying the stylized view of PSFs, and offer the speculation that the rarity of public PSFs stems from institutional barriers, not economic disadvantages. Essay 3 stems from separate research on airline labor relations and analyzes the turnaround of Continental Airlines. A case study reveals Continental's improved employee relations stem from a fundamental change in its authority system, from a traditional hierarchy to a high-involvement system. The case also discusses likely facilitators of this transformation of Continental's authority system. Taken together the essays offer a broad conjecture for future research: that allocation of authority inside the firm may be a more important factor in employee incentives than allocation of ownership to employees. / by Andrew Gustaf von Nordenflycht. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 97-103). / This thesis presents new methods for exploring data using visualization techniques. The first part of the thesis develops a procedure for visualizing the sampling variability of a plot. The motivation behind this development is that reporting a single plot of a sample of data without a description of its sampling variability can be uninformative and misleading in the same way that reporting a sample mean without a confidence interval can be. Next, the thesis develops a method for simplifying large scatter plot matrices, using similar techniques as the above procedure. The second part of the thesis introduces a new diagnostic method for regression called backward selection search. Backward selection search identifies a relevant feature set and a set of influential observations with good accuracy, given the difficulty of the problem, and additionally provides a description, in the form of a set of plots, of how the regression inferences would be affected with other model choices, which are close to optimal. This description is useful, because an observation, that one analyst identifies as an outlier, could be identified as the most important observation in the data set by another analyst. The key idea behind backward selection search has implications for methodology improvements beyond the realm of visualization. This is described following the presentation of backward selection search. Real and simulated examples, provided throughout the thesis, demonstrate that the methods developed in the first part of the thesis will improve the effectiveness and validity of data visualization, while the methods developed in the second half of the thesis will improve analysts' abilities to select robust models. / by Rajiv Menjoge. / Ph.D.