This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 213-221). / The availability and prevalence of data have provided a substantial opportunity for decision makers to improve decisions and outcomes by effectively using this data. In this thesis, we propose approaches that start from data leading to high-quality decisions and predictions in various application areas. In the first chapter, we consider problems with observational data, and propose variants of machine learning (ML) algorithms that are trained by taking into account decision quality. The traditional approach to such a task has often focused on two-steps, separating the estimation task from the subsequent optimization task which uses these estimated models. Consequently, this approach can miss out on potential improvements in decision quality by considering these tasks jointly. Crucially, this leads to stronger prescriptive performance, particularly for smaller training set sizes, and improves the decision quality by 3 - 5% over other state-of-the-art methods. / We introduce the idea of uncertainty penalization to control the optimism of these methods which improves their performance, and propose finite-sample regret bounds. Through experiments on real and synthetic data sets, we demonstrate the value of this approach. In the second chapter, we consider observational data with decision-dependent uncertainty; in particular, we focus on problems with a finite number of possible decisions (treatments). We present our method of prescriptive trees, that prescribes the best treatment option by learning from observational data while simultaneously predicting counterfactuals. We demonstrate the effectiveness of such an approach using real data for the problem of personalized diabetes management. In the third chapter, we consider stochastic optimization problems when the sample average approximation approach is computationally expensive. / We introduce a novel measure, called the Prescriptive divergence which takes into account the decision quality of the scenarios, and consider scenario reduction in this context. We demonstrate the power of this optimization-based approach on various examples. In the fourth chapter, we present our work on a problem in predictive analytics where we focus on ML problems from a modern optimization perspective. For sparse shape-constrained regression problems, we propose modern optimization based algorithms that are scalable, and recover the true support with high accuracy and low false positive rates. / by Nishanth Mundru. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 82-90). / Social networks have given us the ability to spread messages and influence large populations very easily. Malicious actors can take advantage of social networks to manipulate opinions using artificial accounts, or bots. It is suspected that the 2016 U.S. presidential election was the victim of such social network interference, potentially by foreign actors. Foreign influence bots are also suspected of having attacked European elections. The bots main action was the sharing of politically polarized content in an effort to shift opinions. In this work we present a method to identify coordinated influence campaigns, and quantify the impact of bots on the opinions of users in a social network. First, we provide evidence that modern bots in the social network Twitter coordinate their attacks. They do not create original content, but rather amplify certain human users by disproportionately retweeting them. We design a new algorithm for bot detection, and utilize the Ising model from statistical physics to model the network structure and bot labels. Then, we leverage a model for opinion dynamics in a social network, which we validate by showing that the user opinions predicted by the model align with the opinions of these users' based on their social media posts. Finally, we use the opinion model to calculate how the opinions shift when we remove the bots from the network. Our high level finding is that a small number of bots can have a disproportionate impact on the network opinions. / by Nicolas Guenon des Mesnards / S.M. / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 85-90). / The goal of this thesis was to develop a data type agnostic classification algorithm best suited for problems where there are a large number of similar labels (e.g., classifying a port versus a shipyard). The most common approach to this issue is to simply ignore it, and attempt to fit a classifier against all targets at once (a "flat" classifier). The problem with this technique is that it tends to do poorly due to label similarity. Conversely, there are other existing approaches, known as hierarchical classifiers (HCs), which propose clustering heuristics to group the labels. However, the most common HCs require that a "flat" model be trained a-priori before the label hierarchy can be learned. The primary issue with this approach is that if the initial estimator performs poorly then the resulting HC will have a similar rate of error. / To solve these challenges, we propose three new approaches which learn the label hierarchy without training a model beforehand and one which generalizes the standard HC. The first technique employs a k-means clustering heuristic which groups classes into a specified number of partitions. The second method takes the previously developed heuristic and formulates it as a mixed integer program (MIP). Employing a MIP allows the user to have greater control over the resulting label hierarchy by imposing meaningful constraints. The third approach learns meta-classes by using community detection algorithms on graphs which simplifies the hyper-parameter space when training an HC. Finally, the standard HC methodology is generalized by relaxing the requirement that the original model must be a "flat" classifier; instead, one can provide any of the HC approaches detailed previously as the initializer. / By giving the model a better starting point, the final estimator has a greater chance of yielding a lower error rate. To evaluate the performance of our methods, we tested them on a variety of data sets which contain a large number of similar labels. We observed the k-means clustering heuristic or community detection algorithm gave statistically significant improvements in out-of-sample performance against a flat and standard hierarchical classifier. Consequently our approach offers a solution to overcome problems for labeling data with similar classes. / by Zachary D. Blanks. / S.M. / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 273-284). / This thesis focuses on impactful applications of large-scale optimization in transportation and machine learning. Using both theory and computational experiments, we introduce novel optimization algorithms to overcome the tractability issues that arise in real world applications. We work towards the implementation of these algorithms, through software contributions, public policy work, and a formal study of machine learning interpretability. Our implementation in Boston Public Schools generates millions of dollars in yearly transportation savings and led to important public policy consequences in the United States. This work is motivated by large-scale transportation problems that present significant optimization challenges. In particular, we study the problem of ride-sharing, the online routing of hundreds of thousands of customers every day in New York City. / We also contribute to travel time estimation from origin-destination data, on city routing networks with tens of thousands of roads. We additionally consider the problem of school transportation, the scheduling of hundreds of buses to send tens of thousands of children to school everyday. This transportation problem is related to the choice of school start times, for which we also propose an optimization framework. Building on these applications, we present methodological contributions in large- scale optimization. We introduce state-of-the-art algorithms for scheduling problems with time-window (backbone) and for school bus routing (BiRD). Our work on travel time estimation tractably produces solutions to the inverse shortest path length problem, solving a sequence of second order cone problems. We also present a theoretical and empirical study of the stochastic proximal point algorithm, an alternative to stochastic gradient methods (the de-facto algorithm for large-scale learning). / We also aim at the implementation of these algorithms, through software contributions, public policy work (together with stakeholders and journalists), and a collaboration with the city of Boston. Explaining complex algorithms to decision-makers is a difficult task, therefore we introduce an optimization framework to decomposes models into a sequence of simple building blocks. This allows us to introduce formal measure of the "interpretability" of a large class of machine learning models, and to study tradeoffs between this measure and model performance, the price of interpretability. / by Sébastien Martin. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 191-198). / Big data and the internet are shifting the paradigm in promotional pricing and advertising. The amount of readily available data from both point-of-sale systems and web cookies has grown, enabling a shift from qualitative design to quantitative tools. In this work, we address how firms can utilize the power of analytics to maximize profits in both their offline and online channels. First, we consider an online setting, in which an advertiser can target ads to the customer in question. The goal of the advertiser is to determine how to target the right audience with their ads. We study this problem as a Multi-Armed Bandit problem with periodic budgets, and develop an Optimistic-Robust Learning algorithm with bounded expected regret. Practically, simulations on synthetic and real-world ad data show that the algorithm reduces regret by at least 10-20% compared to benchmarks. / Second, we consider an offline setting, in which a retailer can boost profits through the use of promotion vehicles such as flyers and commercials. The goal of the retailer is to decide how to schedule the right promotion vehicles for their products. We model the problem as a non-linear bipartite matching-type problem, and develop provably-good algorithms: a greedy algorithm and an approximate integer program of polynomial size. From a practical perspective, we test our methods on actual data and show potential profit increases of 2-9%. Third, we explore a supply chain setting, in which a supplier offers vendor funds to a retailer who promotionally prices the product to the customer. Vendor funds are trade deals in which a supplier offers a retailer a short-term discount on a specific product, encouraging the retailer to discount the product. / We model the problem as a bilevel optimization model and show that a pass-through constrained vendor fund mitigates forward-buying and coordinates the supply chain on the short term. Finally, we present a pilot study on the impact of promotional pricing on retail profits. We assess the potential impact of our promotion planning tool on historical data from a large retailer. Our results suggest a 9.94% profit improvement for the retailer. / by Lennart Baardman. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

The objective of this research is to improve the railcar/rail truck interface by developing a low maintenance bearing interface with a favorable friction coefficient. Friction and wear at the center bowl/center plate bearing interface cause high turning moments around curved track, wear of truck components, and increased detrimental dynamic effects. The recommended improvement of the rail truck interface is a set of two steel inserts, one concave and one convex, that can be retrofit to center bowls/center plates. The insert geometry addresses concerns about maintaining favorable pressure distribution on existing components, minimizing overall height increase to accommodate existing infrastructure, and retaining railcar stability. The stability of the railcar upon the design inserts has been ensured when the instantaneous center of rotation of the railcar body is above the railcar center of gravity. The damping ratio provided by the frictional moment within center bowl is 240 and eliminates the possibility of dynamic amplification. Using a 90 inch radius of curvature ensures stability and requires a 0.5 inch diameter reduction of the existing center plate for a gap of 1/16 inch. The increase in railcar height for the specific design is 0.71 inches which can be absorbed by either grinding of the center plate or new manufacturing dimensions. The design is feasible for small travel values corresponding to small vertical gaps at the side bearings. In addition to geometry alterations, the bearing surfaces are coated with a protective metallic layer. The literature suggests that optimum friction coefficients between bearing elements in the center bowl/center plate interface may reduce turning moments of the truck, wear of truck components, and detrimental dynamic effects such as hunting. Axial-torsional tests determined friction coefficient estimates and wear properties for a matrix of various metallic protective coatings and steel. Tungsten carbide-cobalt-chrome has a favorable coefficient of 0.3 under standard center bowl/center plate contact conditions.

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Summary This research uses case study as methodology. The researcher interviews several shopping centers¡¦ managers for a better understanding of shopping center industry in Taiwan. The analysis and comparison of cases is basically based on following points: strategy and managerial philosophy, location, marketing function, product assortment management, service, space design.