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Operations Research - Ph.D. / Sc.D.

Mostrando recursos 1 - 20 de 143

  1. Data-driven methods for personalized product recommendation systems

    Papush, Anna
    The online market has expanded tremendously over the past two decades across all industries ranging from retail to travel. This trend has resulted in the growing availability of information regarding consumer preferences and purchase behavior, sparking the development of increasingly more sophisticated product recommendation systems. Thus, a competitive edge in this rapidly growing sector could be worth up to millions of dollars in revenue for an online seller. Motivated by this increasingly prevalent problem, we propose an innovative model that selects, prices and recommends a personalized bundle of products to an online consumer. This model captures the trade-off between myopic...

  2. Optimization models and methods for storage yard operations in maritime container terminals

    Galle, Virgile
    Container terminals, where containers are transferred between different modes of transportation both on the seaside and landside, are crucial links in intercontinental supply chains. The rapid growth of container shipping and the increasing competitive pressure to lower rates result in demand for higher productivity. In this thesis, we design new models and methods for the combinatorial optimization problems representing storage yard operations in maritime container terminals. The goal is to increase the efficiency of yard cranes by decreasing unproductive container moves (also called relocations). We consider three problems with applicability to real-time operations. First, we study the container relocation problem...

  3. Assortment and inventory optimization : from predictive choice models to near-optimal algorithms

    Aouad, Ali (Mohammed Ali)
    Finding optimal product offerings is a fundamental operational issue in modern retailing, exemplified by the development of recommendation systems and decision support tools. The challenge is that designing an accurate predictive choice model generally comes at the detriment of efficient algorithms, which can prescribe near-optimal decisions. This thesis attempts to resolve this disconnect in the context of assortment and inventory optimization, through theoretical and empirical investigation. First, we tightly characterize the complexity of general nonparametric assortment optimization problems. We reveal connections to maximum independent set and combinatorial pricing problems, allowing to derive strong inapproximability bounds. We devise simple algorithms that...

  4. Airline scheduling and air traffic control : incorporating uncertainty and passenger and airline preferences

    Yan, Chiwei
    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...

  5. Mixed-integer convex optimization : outer approximation algorithms and modeling power

    Lubin, Miles (Miles C.)
    In this thesis, we study mixed-integer convex optimization, or mixed-integer convex programming (MICP), the class of optimization problems where one seeks to minimize a convex objective function subject to convex constraints and integrality restrictions on a subset of the variables. We focus on two broad and complementary questions on MICP. The first question we address is, "what are efficient methods for solving MICP problems?" The methodology we develop is based on outer approximation, which allows us, for example, to reduce MICP to a sequence of mixed-integer linear programming (MILP) problems. By viewing MICP from the conic perspective of modern convex...

  6. An analytics approach to problems in health care

    Kung, Jerry Lai
    Health care expenditures in the United States have been increasing at unsustainable rates for more than thirty years with no signs of abating. Decisions to accept or reject deceased-donor kidneys offered to patients on the kidney transplantation wait-list currently rely on physician experience and intuition. Scoring rules to determine which end-stage liver disease patients are in most dire need of immediate transplantation have been haphazardly designed and reactively modified in an attempt to decrease waitlist mortality and increase fairness for cancer patients. For each of the above problem settings, we propose a framework that takes real-world data as input and...

  7. New applications in Revenue Management

    Thraves Cortés-Monroy, Charles Mark
    Revenue Management (RM) is an area with important advances in theory and practice in the last thirty years. This thesis presents three different new applications in RM with a focus on: the firms' perspective, the government's perspective as a policy maker, and the consumers' perspective (in terms of welfare). In this thesis, we first present a two-part tariff pricing problem faced by a satellite data provider. We estimate unobserved data with parametric density functions in order to generate instances of the problem. We propose a mixed integer programming formulation for pricing. As the problem is hard to solve, we propose...

  8. Adaptive optimization problems under uncertainty with limited feedback

    Flajolet, Arthur
    This thesis is concerned with the design and analysis of new algorithms for sequential optimization problems with limited feedback on the outcomes of alternatives when the environment is not perfectly known in advance and may react to past decisions. Depending on the setting, we take either a worst-case approach, which protects against a fully adversarial environment, or a hindsight approach, which adapts to the level of adversariality by measuring performance in terms of a quantity known as regret. First, we study stochastic shortest path problems with a deadline imposed at the destination when the objective is to minimize a risk...

  9. Combinatorial structures in online and convex optimization

    Gupta, Swati, Ph. D. Massachusetts Institute of Technology
    Motivated by bottlenecks in algorithms across online and convex optimization, we consider three fundamental questions over combinatorial polytopes. First, we study the minimization of separable strictly convex functions over polyhedra. This problem is motivated by first-order optimization methods whose bottleneck relies on the minimization of a (often) separable, convex metric, known as the Bregman divergence. We provide a conceptually simple algorithm, Inc-Fix, in the case of submodular base polyhedra. For cardinality-based submodular polytopes, we show that Inc-Fix can be speeded up to be the state-of-the-art method for minimizing uniform divergences. We show that the running time of Inc-Fix is independent...

  10. A robust optimization approach to online problems

    Korolko, Nikita (Nikita E.)
    In this thesis, we consider online optimization problems that are characterized by incrementally revealed input data and sequential irrevocable decisions that must be made without complete knowledge of the future. We employ a combination of mixed integer optimization (MIO) and robust optimization (RO) methodologies in order to design new efficient online algorithms that outperform state-of-the-art methods for many important practical applications. We empirically demonstrate that RO-based algorithms are computationally tractable for instances of practical size, generate more cost-effective decisions and can simultaneously model a large class of similar online problems due to exceptional modeling power of MIO. In Part I,...

  11. Analytic search methods in online social networks

    Marks, Christopher E. (Christopher Edward)
    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...

  12. From data to decisions in healthcare : an optimization perspective

    Weinstein, Alexander Michael
    The past few decades have seen many methodological and technological advances in optimization, statistics, and machine learning. What is still not well understood is how to combine these tools to take data as inputs and give decisions as outputs. The healthcare arena offers fertile ground for improvement in data-driven decisionmaking. Every day, medical researchers develop and test novel therapies via randomized clinical trials, which, when designed efficiently, can provide evidence for efficacy and harm. Over the last two decades, electronic medical record systems have become increasingly prevalent in hospitals and other care settings. The growth of these and other data...

  13. Data-driven algorithms for operational problems

    Cheung, Wang Chi
    In this thesis, we propose algorithms for solving revenue maximization and inventory control problems in data-driven settings. First, we study the choice-based network revenue management problem. We propose the Approximate Column Generation heuristic (ACG) and Potential Based algorithm (PB) for solving the Choice-based Deterministic Linear Program, an LP relaxation to the problem, to near-optimality. Both algorithms only assume the ability to approximate the underlying single period problem. ACG inherits the empirical efficiency from the Column Generation heuristic, while PB enjoys provable efficiency guarantee. Building on these tractability results, we design an earning-while-learning policy for the online problem under a Multinomial...

  14. Multiserver queueing systems in heavy traffic

    Eschenfeldt, Patrick Clark
    In the study of queueing systems, a question of significant current interest is that of large scale behavior, where the size of the system increases without bound. This regime has becoming increasingly relevant with the rise of massive distributed systems like server farms, call centers, and health care management systems. To minimize underutilization of resources, the specific large scale regime of most interest is one in which the work to be done increases as processing capability increases. In this thesis, we characterize the behavior of two such large scale queueing systems. In the first part of the thesis we consider...

  15. Dynamic trading and behavioral finance

    Remorov, Alexander
    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...

  16. Dynamic trading and behavioral finance

    Remorov, Alexander
    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...

  17. Methods for convex optimization and statistical learning

    Grigas, Paul (Paul Edward)
    We present several contributions at the interface of first-order methods for convex optimization and problems in statistical machine learning. In the first part of this thesis, we present new results for the Frank-Wolfe method, with a particular focus on: (i) novel computational guarantees that apply for any step-size sequence, (ii) a novel adjustment to the basic algorithm to better account for warm-start information, and (iii) extensions of the computational guarantees that hold in the presence of approximate subproblem and/or gradient computations. In the second part of the thesis, we present a unifying framework for interpreting "greedy" first-order methods -- namely...

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

    Yuan, Rong, Ph. D. Massachusetts Institute of Technology
    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;...

  19. Exploration vs. exploitation : reducing uncertainty in operational problems

    Shaposhnik, Yaron
    Motivated by several core operational applications, we introduce a class of multistage stochastic optimization models that capture a fundamental tradeoff between performing work under uncertainty (exploitation) and investing resources to reduce the uncertainty in the decision making (exploration/testing). Unlike existing models, in which the exploration-exploitation tradeoffs typically relate to learning the underlying distributions, the models we introduce assume a known probabilistic characterization of the uncertainty, and focus on the tradeoff of learning exact realizations. In the first part of the thesis (Chapter 2), we study a class of scheduling problems that capture common settings in service environments in which the...

  20. Dynamic learning and optimization for operations management problems

    Wang, He, Ph. D. Massachusetts Institute of Technology
    With the advances in information technology and the increased availability of data, new approaches that integrate learning and decision making have emerged in operations management. The learning-and-optimizing approaches can be used when the decision maker is faced with incomplete information in a dynamic environment. We first consider a network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory constraints. The retailer does not know the exact demand distribution at each price and must learn the distribution from sales data. We propose a dynamic learning and pricing algorithm, which builds upon the Thompson sampling...

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