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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
411

Online optimization in routing and scheduling

Wagner, Michael R. (Michael Robert), 1978- January 2006 (has links)
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (leaves 169-176). / In this thesis we study online optimization problems in routing and scheduling. An online problem is one where the problem instance is revealed incrementally. Decisions can (and sometimes must) be made before all information is available. We design and analyze (polynomial-time) online algorithms for a variety of problems. We utilize worst-case competitive ratio (and relaxations thereof), asymptotic and Monte Carlo simulation analyses in our study of these algorithms. The focus of this thesis is on online routing problems in arbitrary metric spaces. We begin our study with online versions of the Traveling Salesman Problem (TSP) and the Traveling Repairman Problem (TRP). We then generalize these basic problems to allow for precedence constraints, capacity constraints and multiple vehicles. We give the first competitive ratio results for many new online routing problems. We then consider resource augmentation, where we give the online algorithm additional resources: faster servers, larger capacities, more servers, less restrictive constraints and advanced information. We derive new worst-case bounds that are relaxations of the competitive ratio. / (cont.) We also study the (stochastic) asymptotic properties of these algorithms - introducing stochastic structure to the problem data, unknown and unused by the online algorithm. In a variety of situations we show that many online routing algorithms are (quickly) asymptotically optimal, almost surely, and we characterize the rates of convergence. We also study classic machine sequencing problems in an online setting. Specifically, we look at deterministic and randomized algorithms for the problems of scheduling jobs with release dates on single and parallel machines, with and without preemption, to minimize the sum of weighted completion times. We derive improved competitive ratio bounds and we show that many well-known machine scheduling algorithms are almost surely asymptotically optimal under general stochastic assumptions. For both routing and sequencing problems, we complement these theoretical derivations with Monte Carlo simulation results. / by Michael Robert Wagner. / Ph.D.
412

Optimization-simulation framework to optimize hospital bed allocation in academic medical centers

Vanden Berg, Andrew M January 2018 (has links)
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 99-100). / Congestion, overcrowding, and increasing patient wait times are major challenges that many large, academic centers currently face. To address these challenges, hospitals must effectively utilize available beds through proper strategic bed allocation and robust operational day-to-day bed assignment policies. Since patient daily demand for beds is highly variable, it is frequent that the physical capacity allocated to a given clinical service is not sufficient to accommodate all of the patients who belong to that service. This situation could lead to extensive wait time of patients in various locations in the hospital (e.g., the emergency department), as well as clinically and operationally undesirable misplacements of patients in hospital floors/beds that are managed by other clinical services than the ones to which the patients belong. In this thesis, we develop an optimization-simulation framework to optimize the bed allocation at Mass General Hospital. Detailed, data-driven simulation suggests that the newly proposed bed allocation would lead to significant reduction in patient intra-day wait time in the emergency department and other hospital locations, as well as a major reduction in the misplacements of patients in the Medicine service, which is the largest service in the hospital. We employ a two-pronged approach. First, we developed a detailed simulation setting of the entire hospital that could be used to assess the effectiveness of day-to-day operational bed assignment policies given a specific bed allocation. However, the simulation does not allow tractable optimization that seeks to find the best bed allocation among all possible allocations. This motivates the development of a network-flow/network design inspired mixed integer program that approximates the operational performance of bed allocations and allows us to effectively search for approximately the best allocation. The mixed integer program can be solved via a scenario sampling approach to provide candidate bed allocations. These are then tested and evaluated via the simulation setting. These tools facilitate expert discussions on how to modify the existing bed allocation at MGH to improve the day-to-day performance of the bed assignment process. / by Andrew M. Vanden Berg. / S.M.
413

Application of robust statistics to asset allocation models

Zhou, Xinfeng January 2006 (has links)
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (p. 105-107). / Many strategies for asset allocation involve the computation of expected returns and the covariance or correlation matrix of financial instruments returns. How much of each instrument to own is determined by an attempt to minimize risk (the variance of linear combinations of investments in these financial assets) subject to various constraints such as a given level of return, concentration limits, etc. The expected returns and the covariance matrix contain many parameters to estimate and two main problems arise. First, the data will very likely have outliers that will seriously affect the covariance matrix. Second, with so many parameters to estimate, a large number of observations are required and the nature of markets may change substantially over such a long period. In this thesis we use robust covariance procedures, such as FAST-MCD, quadrant-correlation-based covariance and 2D-Huber-based covariance, to address the first problem and regularization (Bayesian) methods that fully utilize the market weights of all assets for the second. High breakdown affine equivariant robust methods are effective, but tend to be costly when cross-validation is required to determine regularization parameters. / (cont.) We, therefore, also consider non-affine invariant robust covariance estimation. When back-tested on market data, these methods appear to be effective in improving portfolio performance. In conclusion, robust asset allocation methods have great potential to improve risk-adjusted portfolio returns and therefore deserve further exploration in investment management research. / by Xinfeng Zhou. / S.M.
414

Algorithms for large-scale personalization

Li, Andrew A. (Andrew Andi) January 2018 (has links)
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 191-205). / The term personalization typically refers to the activity of online recommender systems, and while product and content personalization is now ubiquitous in e-commerce, systems today remain relatively primitive: they are built on a small fraction of available data, run with heuristic algorithms, and restricted to e-commerce applications. This thesis addresses key challenges and new applications for modern, large-scale personalization. In particular, this thesis is outlined as follows: First, we formulate a generic, flexible framework for learning from matrix-valued data, including the kinds of data commonly collected in e-commerce. Underlying this framework is a classic de-noising problem called tensor recovery, for which we provide an efficient algorithm, called Slice Learning, that is practical for massive datasets. Further, we establish near-optimal recovery guarantees that represent an order improvement over the best available results for this problem. Experimental results from a music recommendation platform are shown. Second, we apply this de-noising framework to new applications in precision medicine where data are routinely complex and in high dimensions. We describe a simple, accurate proteomic blood test (a 'liquid biopsy') for cancer detection that relies on de-noising via the Slice Learning algorithm. Experiments on plasma from healthy patients that were later diagnosed with cancer demonstrate that our test achieves diagnostically significant sensitivities and specificities for many types of cancers in their earliest stages. Third, we present an efficient, principled approach to operationalizing recommendations, i.e. the decision of exactly what items to recommend. Motivated by settings such as online advertising where the space of items is massive and recommendations must be made in milliseconds, we propose an algorithm that simultaneously achieves two important properties: (1) sublinear runtime and (2) a constant-factor guarantee under a wide class of choice models. Our algorithm relies on a new sublinear time sampling scheme, which we develop to solve a class of problems that subsumes the classic nearest neighbor problem. Results from a massive online content recommendation firm are given. Fourth, we address the problem of cost-effectively executing a broad class of computations on commercial cloud computing platforms, including the computations typically done in personalization. We formulate this as a resource allocation problem and introduce a new approach to modeling uncertainty - the Data-Driven Prophet Model - that treads the line between stochastic and adversarial modeling, and is amenable to the common situation where stochastic modeling is challenging, despite the availability of copious historical data. We propose a simple, scalable algorithm that is shown to be order-optimal in this setting. Results from experiments on a commercial cloud platform are shown. / by Andrew A. Li. / Ph. D.
415

Regulating exploration in multi-armed bandit problems with time patterns and dying arms

Tracà, Stefano January 2018 (has links)
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 65-70). / In retail, there are predictable yet dramatic time-dependent patterns in customer behavior, such as periodic changes in the number of visitors, or increases in customers just before major holidays. The standard paradigm of multi-armed bandit analysis does not take these known patterns into account. This means that for applications in retail, where prices are fixed for periods of time, current bandit algorithms will not suffice. This work provides a framework and methods that take the time-dependent patterns into account. In the corrected methods, exploitation (greed) is regulated over time, so that more exploitation occurs during higher reward periods, and more exploration occurs in periods of low reward. In order to understand why regret is reduced with the corrected methods, a set of bounds on the expected regret are presented and insights into why we would want to exploit during periods of high reward are discussed. When the set of available options changes over time, mortal bandits algorithms have proven to be extremely useful in a number of settings, for example, for providing news article recommendations, or running automated online advertising campaigns. Previous work on this problem showed how to regulate exploration of new arms when they have recently appeared, but they do not adapt when the arms are about to disappear. Since in most applications we can determine either exactly or approximately when arms will disappear, we can leverage this information to improve performance: we should not be exploring arms that are about to disappear. Also for this framework, adaptations of algorithms and regret bounds are provided. The proposed methods perform well in experiments, and were inspired by a high-scoring entry in the Exploration and Exploitation 3 contest using data from Yahoo! Front Page. That entry heavily used time-series methods to regulate greed over time, which was substantially more effective than other contextual bandit methods. / by Stefano Tracà. / Ph. D.
416

Dynamic pricing and inventory control with no backorders under uncertainty and competition

Adida, Elodie January 2006 (has links)
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (p. 271-284). / Recently, revenue management has become popular in many industries such as the airline, the supply chain, and the transportation industry. Decision makers realize that even small improvements in their operations can have a significant impact on their profits. Nevertheless, determining pricing and inventory optimal policies in more realistic settings may not be a tractable task. Ignoring the potential inaccuracy of parameters may lead to a solution that actually performs poorly, or even that violates some constraints. Finally, competitors impact a supplier's best strategy by influencing her demand, revenues, and field of possible actions. Taking a game theoretic approach and determining the equilibrium of the system can help understand its state in the long run. This thesis presents a continuous time optimal control model for studying a dynamic pricing and inventory control problem in a make-to-stock manufacturing system. We consider a multi-product capacitated, dynamic setting. We introduce a demand-based model with convex costs. A key part of the model is that no backorders are allowed, as this introduces a constraint on the state variables. We first study the deterministic version of this problem. / (cont.) We introduce and study a solution method that enables to compute the optimal solution on a finite time horizon in a monopoly setting. Our results illustrate the role of capacity and the effects of the dynamic nature of demand. We then introduce an additive model of demand uncertainty. We use a robust optimization approach to protect the solution against data uncertainty in a tractable manner, and without imposing stringent assumptions on available information. We show that the robust formulation is of the same order of complexity as the deterministic problem and demonstrate how to adapt solution method. Finally, we consider a duopoly setting and use a more general model of additive and multiplicative demand uncertainty. We formulate the robust problem as a coupled constraint differential game. Using a quasi-variational inequality reformulation, we prove the existence of Nash equilibria in continuous time and study issues of uniqueness. Finally, we introduce a relaxation-type algorithm and prove its convergence to a particular Nash equilibrium (normalized Nash equilibrium) in discrete time. / by Elodie Adida. / Ph.D.
417

Velocity-based storage and stowage decisions in a semi-automated fulfillment system

Yuan, Rong, Ph. D. Massachusetts Institute of Technology January 2016 (has links)
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 153-156). / The supply chain management for an online retailing business is centered around the operations of its fulfillment centers. A fulfillment center receives and holds inventory from vendors, and then uses this inventory to fill customer orders. Our research focuses on a new operating architecture of an order fulfillment system, enabled by new technology. We refer to it as the Semi-automated Fulfillment System. Different from the person-to-goods model in traditional warehouses, the semi-automated fulfillment system adopts a goods-to-person model for stowing and picking items from a storage field. In a semi-automated fulfillment system the inventory is stored on mobile storage pods; those mobile pods are then carried by robotic drives to static stations at which the operators conduct pick or stow operations. In the first chapter, we describe and identify three key operational decisions in the semiautomated fulfillment system, namely from which pods to pick the inventory needed (picking decision), where to return the pod to the storage field upon the completion of a pick or stow operation (storage decision), and to which pods to replenish the received inventory (stowage decision). We present a high-level capacity planning model for determining the number of robotic drives needed to achieve a given throughput level. This model highlights how the operational efficiency in this system depends on two key parameters, namely the travel time for an entire drive trip and the number of unit picks or stows per pod trip. In the second chapter, we focus on the storage decisions. The storage decision is to decide to which storage location to return a pod upon the completion of a pick or stow operation. We extend the academic results on the benefits of adopting velocity-based and class-based storage policies to the context of the semi-automated fulfillment system. We associate with each storage pod a velocity measure that represents an expectation of the number of picks from that pod in the near future. We then show that by assigning the high velocity pods to the most desirable storage locations, we can significantly reduce the drive travel time, compared to the random storage policy that returns the pod to a randomly-chosen storage location. We show that class-based storage policies with two or three classes, can achieve most of the benefits from the idealized velocity-based policy. Furthermore, we characterize how the performance of the velocity-based and class-based storage policies depend on the velocity variability across the storage pods; in particular we model how the benefits from velocity-based storage policies increase with increased variation in the pod velocities. In the third chapter, we build a discrete-time simulator to validate the theoretical models in the second chapter with real industry data. We observe a 6% to 11% reduction in the travel distance with 2-class or 3-class system, depending on the parameter settings. From a sensitivity analysis we establish the robustness of the class-based storage policies as they continue to perform well under a broad range of warehouse settings including different zoning strategies, resource utilization levels and space utilization levels. In the fourth chapter, we examine two stowage decisions, one at the zone level and the other at the pod level. The zone-level decision is to decide how to allocate the received inventory to multiple storage zones. The objective is to assure that the resulting picking workload for each zone is within its capacity. We show by simulation that a chaining-based allocation can be effective to balance the picking workload across different storage zones. The pod-level stowage decision is to decide on which pods to stow the inventory. We formulate a mixedinteger program (MIP) to find the optimal stowage profile that maximizes the number of unit picks per pod trip. We solve the MIP for a set of test cases to gain insight into the structure of optimal stowage policy. Motivated by these insights, we further propose a class-based stowage process that induces variability across the pod velocities. / by Rong Yuan. / Ph. D.
418

Analytics for financing drug development

Fagnan, David Erik January 2015 (has links)
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 133-139). / Financing drug development has a particular set of challenges including long development times, high chance of failure, significant market valuation uncertainty, and high costs of development. The earliest stages of translational research pose the greatest risks, which have been termed the "valley of death" as a result of a lack of funding. This thesis focuses on an exploration of financial engineering techniques aimed at addressing these concerns. Despite the recent financial crisis, many suggest that securitization is an appropriate tool for financing such large social challenges. Although securitization has been demonstrated effectively at later stages of drug development for drug royalties of approved drugs, it has yet to be utilized at earlier stages. This thesis starts by extending the model of drug development proposed by Fernandez et al. (2012). These extensions significantly influence the resulting performance and optimal securitization structures. Budget-constrained venture firms targeting high financial returns are incentivized to fund only the best projects, thereby potentially stranding less-attractive projects. Instead, such projects have the potential to be combined in larger portfolios through techniques such as securitization which reduce the cost of capital. In addition to modeling extensions, we provide examples of a model calibrated to orphan drugs, which we argue are particularly suited to financial engineering techniques. Using this model, we highlight the impact of our extensions on financial performance and compare with previously published results. We then illustrate the impact of incorporating a credit enhancement or guarantee, which allows for added flexibility of the capital structure and therefore greater access to lower costing capital. As an alternative to securitization, we provide some examples of a structured equity approach, which may allow for increased access to or efficiency of capital by matching investor objectives. Finally, we provide examples of optimizing the Sortino ratio through constrained Bayesian optimization. / by David Erik Fagnan. / Ph. D.
419

New algorithms in machine learning with applications in personalized medicine

Zhuo, Ying Daisy January 2018 (has links)
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 165-173). / Recent advances in machine learning and optimization hold much promise for influencing real-world decision making, especially in areas such as health care where abundant data are increasingly being collected. However, imperfections in the data pose a major challenge to realizing their full potential: missing values, noisy observations, and unobserved counterfactuals all impact the performance of data-driven methods. In this thesis, with a fresh perspective from optimization, I revisit some of the well-known problems in statistics and machine learning, and develop new methods for prescriptive analytics. I show examples of how common machine learning tasks, such as missing data imputation in Chapter 2 and classication in Chapter 3, can benet from the added edge of rigorous optimization formulations and solution techniques. In particular, the proposed opt.impute algorithm improves imputation quality by 13.7% over state-of-the-art methods, as averaged over 95 real data sets, which leads to further performance gains in downstream tasks. The power of prescriptive analytics is shown in Chapter 4 by our approach to personalized diabetes management, which identifies response patterns using machine learning and individualizes treatments via optimization. These newly developed machine learning algorithms not only demonstrate improved performance in large-scale experiments, but are also applied to solve the problems in health care that motivated them. Our simulated trial for diabetic patients in Chapter 4 demonstrates a clinically relevant reduction in average hemoglobin A1c levels compared to current practice. Finally, when predicting mortality for cancer patients in Chapter 5, applying opt.impute on missing data along with the cutting-edge algorithm Optimal Classication Tree on a rich data set prepared from electronic medical records, we are able to accurately risk stratify patients, providing physicians with interpretable insights and valuable risk estimates at time of treatment decisions and end-of-life planning. / by Ying Daisy Zhuo. / Ph. D.
420

The dynamics of global financial crises

Amonlirdviman, Kevin, 1975- January 2002 (has links)
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2002. / Includes bibliographical references (p. 57-58). / This thesis presents a Markov chain model of the transmission of financial crises. Using bilateral trade data and a measure of exchange market pressure, it develops a method to determine a set of transition probabilities that describe the crisis transmission dynamics. The dynamics are characterized by one month conditional crisis probabilities and the probability of a crisis occurring within one year. Calculations of the transition probabilities for a three country example suggest that minor trading partners can increase the likelihood of a crisis in the home country through their effect on major trading partners. / by Kevin Amonlirdviman. / S.M.

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