Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014. / 85 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 99-104). / Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviors from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response to disease spread. We couple the disease spread and panic spread processes and model them through local interactions between agents. The behavioral contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analyzing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City, the 2003 SARS and 2009 H1N1 outbreaks in Hong Kong and the 2012-2013 Boston influenza season, accurately predicting population-level behavior. The effect of interventions on the disease spread and social response is explored, and we implement an optimization study to determine the least cost intervention, taking into account the costs of the disease itself, the intervention and the social response. We show that the optimal strategy is dependent upon the relative costs assigned to infection with the disease, intervention and social response, as well as the perceived risk of infection. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks. / by Shannon M. Fast. / S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015. / 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 139-156). / Cancer is a leading cause of death both in the United States and worldwide. In this thesis we use machine learning and optimization to identify effective treatments for advanced cancers and to identify effective screening strategies for detecting early-stage disease. In Part I, we propose a methodology for designing combination drug therapies for advanced cancer, evaluating our approach using advanced gastric cancer. First, we build a database of 414 clinical trials testing chemotherapy regimens for this cancer, extracting information about patient demographics, study characteristics, chemotherapy regimens tested, and outcomes. We use this database to build statistical models to predict trial efficacy and toxicity outcomes. We propose models that use machine learning and optimization to suggest regimens to be tested in Phase II and III clinical trials, evaluating our suggestions with both simulated outcomes and the outcomes of clinical trials testing similar regimens. In Part II, we evaluate how well the methodology from Part I generalizes to advanced breast cancer. We build a database of 1,490 clinical trials testing drug therapies for breast cancer, train statistical models to predict trial efficacy and toxicity outcomes, and suggest combination drug therapies to be tested in Phase II and III studies. In this work we model differences in drug effects based on the receptor status of patients in a clinical trial, and we evaluate whether combining clinical trial databases of different cancers can improve clinical trial toxicity predictions. In Part III, we propose a methodology for decision making when multiple mathematical models have been proposed for a phenomenon of interest, using our approach to identify effective population screening strategies for prostate cancer. We implement three published mathematical models of prostate cancer screening strategy outcomes, using optimization to identify strategies that all models find to be effective. / by John Silberholz. / Ph. D.

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 175-185). / This thesis presents and evaluates methods for searching and analyzing social media data in order to improve situational awareness. We begin by proposing a method for network vertex search that looks for the target vertex by sequentially examining the neighbors of a set of "known" vertices. Using a dynamic programming approach, we show that there is always an optimal "block" search policy, in which all of the neighbors of a known vertex are examined before moving on to another vertex. We provide a precise characterization of the optimal policy in two specific cases: (1) when the connections between the known vertices and the target vertex are independent, and (2) when the target vertex is connected to at most one known vertex. We then apply this result to the problem of finding new accounts belonging to Twitter users whose previous accounts had been suspended for extremist activity, quantifying the performance of our optimal search policy in this application against other policies. In this application we use thousands of Twitter accounts related to the Islamic State in Iraq and Syria (ISIS) to develop a behavioral models for these extremist users. These models are used to identify new extremist accounts, identify pairs of accounts belonging to the same user, and predict to whom a user will connect when opening an account. We use this final model to inform our network search application. Finally, we develop a more general application of network search and classification that obtains a set of social media users from a specified location or group. We propose an expand -- classify methodology which recursively collects users that have social network connections to users inside the target location, and then classifies all of the users by maximizing the probability over a factor graph model. This factor graph model accounts for the implications of both observed user profile features and social network connections in inferring location. Using geo-located data to evaluate our method, we find that our classification method typically outperforms Twitter's native search methods in building a dataset of Twitter users in a specific location. / by Christopher E. Marks. / Ph. D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004. / Includes bibliographical references (p. 143-145). / The United States Air Force's Air Mobility Command is responsible for creating a schedule and executing that schedule for a large-scale air mobility network that encompasses different mission areas. One of the mission areas is channel route. Channel route execution often experiences disruptions that motivate a need for changes in the current channel route schedule. Traditionally, re-planning the channel route schedule has been a manual process that usually stops after the first feasible set of changes is found, due to the challenges of large amounts of data and urgency for a re-plan. Other challenges include subjective trade-offs and a desire for minimal changes to the channel route schedule. We re-plan the channel route schedule using a set of integer programs and heuristics that overcomes these challenges. The integer programs' variables incorporate many of Air Mobility Command's operating constraints, so they do not have to be explicitly included in the formulations. The re-plan uses opportunities in the other mission areas and reroutes channel route aircraft. Finally, our methods can quickly find a solution, allow for "what-if' analysis and interaction with the user, and can be adapted to an evolution in Air Mobility Command's operations while the underlying models remain constant. / by Corbin G. Koepke. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 63-69). / In this thesis, we present algorithms to model and identify shared patterns in human activity with respect to three applications. First, we propose a novel model to characterize the bursty dynamics found in human activity. This model couples excitation from past events with weekly periodicity and circadian rhythms, giving the first descriptive understanding of mechanisms underlying human behavior. The proposed model infers directly from event sequences both the transition rates between tasks as well as nonhomogeneous rates depending on daily and weekly cycles. We focus on credit card transactions to test the model, and find it performs well in prediction and is a good statistical fit for individuals. Second, using credit card transactions, we identify lifestyles in urban regions and add temporal context to behavioral patterns. We find that these lifestyles not only correspond to demographics, but also have a clear signal with one's social network. Third, we analyze household load profiles for segmentation based on energy consumption, focusing on capturing peak times and overall magnitude of consumption. We propose novel metrics to measure the representative accuracy of centroids, and propose a method that outperforms standard and state of the art baselines with respect to these metrics. In addition, we show that this method is able to separate consumers well based on their solar PV and storage needs, thus helping consumers understand their needs and assisting utilities in making good recommendations. / by Sharon Xu. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000. / Also available online at the DSpace at MIT website. / Includes bibliographical references (p. 119-122). / Organizations perform work essentially through operations and projects. The characteristics of projects makes them extremely difficult to manage: their non repetitive nature discards the trial and error learning, while their short life span is particularly unforgiving to misjudgments. Some authors have found that effective scheduling is an important contributor to the success of research and development (R&D), as well as construction projects. The widely used critical path method for scheduling projects and identifying important activities fails to capture two important dimensions of the problem: the availability of different technologies (or options) to perform the activities, and the inherent problem of limited availability of resources that most managers face. Nevertheless, when one tries to account for such additional constraints, the problems become very hard to solve. In this thesis we propose an approach to the scheduling problem using a genetic algorithm, and try to compare its performance to more traditional approaches, such as an extension to a very innovative Lagrangian relaxation approach recently proposed. The purpose of using genetic algorithms is twofold: first to obtain good approximations to very hard problems, and second to realize the limitations and virtues of this search technique. The purpose of this thesis is not only to develop the algorithms, but also to obtain insight about the implications of the additional constraints in the perspective of a project manager. / by Eduardo B. Messmacher. / S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018. / 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 183-188). / This thesis explores a variety of techniques for large-scale stochastic control. These range from simple heuristics that are motivated by the problem structure and are amenable to analysis, to more general deep reinforcement learning (RL) which applies to broader classes of problems but is trickier to reason about. In the first part of this thesis, we explore a less known application of stochastic control in Multi-armed bandits. By assuming a Bayesian statistical model, we get enough problem structure so that we can formulate an MDP to maximize total rewards. If the objective involved total discounted rewards over an infinite horizon, then the celebrated Gittins index policy would be optimal. Unfortunately, the analysis there does not carry over to the non-discounted, finite-horizon problem. In this work, we propose a tightening sequence of 'optimistic' approximations to the Gittins index. We show that the use of these approximations together with the use of an increasing discount factor appears to offer a compelling alternative to state-of-the-art algorithms. We prove that these optimistic indices constitute a regret optimal algorithm, in the sense of meeting the Lai-Robbins lower bound, including matching constants. The second part of the thesis focuses on the collateral management problem (CMP). In this work, we study the CMP, faced by a prime brokerage, through the lens of multi-period stochastic optimization. We find that, for a large class of CMP instances, algorithms that select collateral based on appropriately computed asset prices are near-optimal. In addition, we back-test the method on data from a prime brokerage and find substantial increases in revenue. Finally, in the third part, we propose novel deep reinforcement learning (DRL) methods for option pricing and portfolio optimization problems. Our work on option pricing enables one to compute tighter confidence bounds on the price, using the same number of Monte Carlo samples, than existing techniques. We also examine constrained portfolio optimization problems and test out policy gradient algorithms that work with somewhat different objective functions. These new objectives measure the performance of a projected version of the policy and penalize constraint violation. / by Eli Gutin. / Ph. D.

<p> Supply chains are vulnerable to disruptions at any stage of the distribution system. These disruptions can be caused by natural disasters, production problems, or labor defects. The consequences of these disruptions may result in significant economic losses or even human deaths. Therefore, it is important to consider any disruption as an important factor in strategic supply chain design. Consequently, the primary outputs of this dissertation include insights for designing robust supply chains that are neither significantly nor adversely impacted by disruptions.</p><p> The impact of correlated supplier failures is examined and how this problem can be modeled as a variant of a facility location problem is described. Two main problems are defined, the first being the design of a robust supply chain, and the second being the optimization of operational inspection schedules to maintain the quality of an already established supply chain. In this regard, both strategic and operational decisions are considered in the model and (1) a two-stage stochastic programming model; (2) a multi-objective stochastic programming model; and (3) a dynamic programming model are developed to explore the tradeoffs between cost and risk.</p><p> Three methods are developed to identify optimal and robust solutions: an integer L-shaped method; a hybrid genetic algorithm using Data Envelopment Analysis; and an approximate dynamic programming method. Several sensitivity analyses are performed on the model to see how the model output would be affected by uncertainty.</p><p> The findings from this dissertation will be able to help both practitioners designing supply chains, as well as policy makers who need to understand the impact of different disruption mitigation strategies on cost and risk in the supply chain.</p><p>

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (p. 121-128). / Human decision making is a topic of great interest to marketers, psychologists, economists, and others. People are often modeled as rational utility maximizers with unlimited mental resources. However, due to the structure of the environment as well as cognitive limitations, people frequently use simplifying heuristics for making quick yet accurate decisions. In this research, we apply discrete optimization to infer from observed data if a person is behaving in way consistent with a choice heuristic (e.g., a noncompensatory lexicographic decision rule). We analyze the computational complexity of several inference related problems, showing that while some are easy due to possessing a greedoid language structure, many are hard and likely do not have polynomial time solutions. For the hard problems we develop an exact dynamic programming algorithm that is robust and scalable in practice, as well as analyze several local search heuristics. We conduct an empirical study of SmartPhone preferences and find that the behavior of many respondents can be explained by lexicographic strategies. / (cont.) Furthermore, we find that lexicographic decision rules predict better on holdout data than some standard compensatory models. Finally, we look at a more general form of noncompensatory decision process in the context of consideration set formation. Specifically, we analyze the computational complexity of rule-based consideration set formation, develop solution techniques for inferring rules given observed consideration data, and apply the techniques to a real dataset. / by Michael J. Yee. / Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. / Includes bibliographical references (p. 65-67). / We develop a simulation model based on patient data from 2/1/05 to 1/31/06 that represents the operations of the Emergency Department at Beth Israel Deaconess Medical Center, a Harvard teaching hospital and a leading medical institution. The model uses a multiclass representation of patients, a time-varying arrival process module that uses multivariate regression to predict future patient arrivals, and a service module that takes into account the fact that service times decrease and capacity increases when the system becomes congested. We show that the simulation model results in predictions of waiting times that closely match those observed in the data. Most importantly, we use the simulation model to propose and analyze new policies such as increasing the number of beds, reducing the downtime between patients, and introducing a point of care lab testing device. The model predicts that incorporating a suite of these proposed changes will result in 21% reduction in waiting times. / by Clay W. Noyes. / S.M.