Theses - Operations Research
http://hdl.handle.net/1721.1/7902
2016-03-25T15:05:28ZApplications of optimal portfolio management
http://hdl.handle.net/1721.1/101292
Applications of optimal portfolio management
Bisias, Dimitrios
This thesis revolves around applications of optimal portfolio theory. In the first essay, we study the optimal portfolio allocation among convergence trades and mean reversion trading strategies for a risk averse investor who faces Value-at-Risk and collateral constraints with and without fear of model misspecification. We investigate the properties of the optimal trading strategy, when the investor fully trusts his model dynamics. Subsequently, we investigate how the optimal trading strategy of the investor changes when he mistrusts the model. In particular, we assume that the investor believes that the data will come from an unknown member of a set of unspecified alternative models near his approximating model. The investor believes that his model is a pretty good approximation in the sense that the relative entropy of the alternative models with respect to his nominal model is small. Concern about model misspecification leads the investor to choose a robust optimal portfolio allocation that works well over that set of alternative models. In the second essay, we study how portfolio theory can be used as a framework for making biomedical funding allocation decisions focusing on the National Institutes of Health (NIH). Prioritizing research efforts is analogous to managing an investment portfolio. In both cases, there are competing opportunities to invest limited resources, and expected returns, risk, correlations, and the cost of lost opportunities are important factors in determining the return of those investments. Can we apply portfolio theory as a systematic framework of making biomedical funding allocation decisions? Does NIH manage its research risk in an efficient way? What are the challenges and limitations of portfolio theory as a way of making biomedical funding allocation decisions? Finally in the third essay, we investigate how risk constraints in portfolio optimization and fear of model misspecification affect the statistical properties of the market returns. Risk sensitive regulation has become the cornerstone of international financial regulations. How does this kind of regulation affect the statistical properties of the financial market? Does it affect the risk premium of the market? What about the volatility or the liquidity of the market?
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 183-188).
2015-01-01T00:00:00ZPricing for retail, social networks and green technologies
http://hdl.handle.net/1721.1/101291
Pricing for retail, social networks and green technologies
Cohen, Maxime C
What is the right price to charge your customers? Many retailers and researchers are facing this question. In the last three decades, tremendous progress was made, both in the academic and business worlds. In this thesis, we investigate four novel pricing applications. In the first part, we study the promotion optimization problem for supermarket retailers. One needs to decide which products to promote, the depth of price discounts and when to schedule the promotions. To capture the stockpiling behavior of consumers, we propose two general classes of demand functions that can be easily estimated from data. We then develop an approximation that allows us to solve the problem efficiently and derive analytical results on its accuracy. The second part is motivated by the ubiquity of social networking platforms. We consider a setting where a monopolist sells an indivisible good to consumers embedded in a social network. First, the firm designs prices to maximize its profits. Subsequently, consumers choose whether to purchase the item or not. Assuming positive externalities, we show the existence of a pure Nash equilibrium. Using duality theory and integer programming techniques, we reformulate the problem into a linear mixed-integer program. We then derive efficient ways of optimally solving the problem for discriminative and uniform pricing strategies. The third part considers the problem of pricing a product for which demand information is very limited. We impose minimal assumptions on the problem: that is, only the constant marginal cost and the maximal price the firm can set are known. We propose a simple way of pricing the product by approximating the true inverse demand by a linear function. Surprisingly, we show that this approximation yields a good profit performance for a wide range of demand curves. In the final part, we consider green technology products such as electric vehicles. We propose a Stackelberg model where the government offers consumer subsidies in order to encourage the technology adoption, whereas the supplier decides price and production to maximize profits. We address the question: How does demand uncertainty affect the government, the industry and the consumers, when designing policies.
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.
2015-01-01T00:00:00ZAnalytics for Improved Cancer Screening and Treatment
http://hdl.handle.net/1721.1/101290
Analytics for Improved Cancer Screening and Treatment
Silberholz, John
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.
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).
2015-01-01T00:00:00ZOptimization of yard operations in maritime container terminals
http://hdl.handle.net/1721.1/99589
Optimization of yard operations in maritime container terminals
Borjian Boroujeni, Setareh
With the continuous growth in international container shipping, many container terminals in maritime ports face congestion, particularly during peak hours of service, and when there is limited space in the storage area. Thus, there has been increasing interest in improving operations efficiency in container terminals. An efficient terminal, in general, is one that discharges containers from the ships in a timely manner and delivers containers to customers with a reasonable wait time. Moreover, a key performance measure in the storage area is the number of moves performed by yard cranes. Due to limited space in the storage area, containers are stacked on top of each other, forming a column of containers that can be accessed by yard cranes only from the top. Therefore, in order to retrieve a container that is covered by other containers, the blocking containers must be relocated to other slots. Because such relocation moves are costly for the port operators and result in service delays, one of the main challenges in the storage area is to plan the moves such that the number of relocations is minimized. This problem is referred to as the Container Relocation Problem (CRP). The CRP in its most simplified setting is concerned with finding a sequence of moves that retrieves all containers in a pre-defined order with a minimum number of relocations, assuming that no new containers are stacked during the retrieval process. Also, it is often assumed that the non-blocking containers cannot be relocated (i.e., repositioning moves are not allowed), an assumption that can result in a sub-optimal solution. Other variants of the container relocation problem include the dynamic CRP and the CRP with incomplete information. The former involves minimizing the number of relocations when containers are continuously stacked in and retrieved from the storage area, and the latter refers to the case that the departure times of containers are not fully known in advance. For example, a probabilistic distribution of container departure orders, or approximate departure times (in the form of time windows) might be known. Another important efficiency metric, in addition to the number of relocations, is customer wait times during the retrieval process. In particular, when repositioning moves are allowed in the system, there is a trade-off between the total number of relocations (including repositionings) and wait times, because such repositioning moves make the retrieval process faster for trucks arriving in the future. Also, it might be desired to prioritize some customers so that those prioritized experience shorter wait times. For example, in terminals with appointment systems, shorter waiting time guarantees can be given to customers who book in advance a time slot for picking up their containers. In this thesis, we propose optimization models that capture service-based and cost-based objectives and study different service policies. In the first part of this thesis, we study the CRP with complete information using an optimization model and heuristic approach. In particular, we formulate CRP (with no restrictive assumptions on repositioning moves) as an Integer Program that minimizes the weighted sum of the number of relocations and the total wait time of customers. Our integer program provides the optimal sequence of moves for retrieving containers subject to various service policies. For example, it can be used by port operators to minimize customer wait times, or to give different waiting time guarantees to different customers to reflect relative priorities. Moreover, by assigning different weight factors to the two objectives, one can use our model to plan repositioning moves. We also extend our model to the dynamic CRP and illustrate how the flexibility in the stacking process can be exploited to optimize jointly the sequence of moves and the stacking position of containers. Additionally, we propose a class of flexible retrieval policies. We demonstrate that flexible policies can result in fewer relocations and shorter wait times, thereby benefiting both the port operators and customers. In the second part of this thesis, we study the CRP with incomplete information in a 2-stage setting where the departure times of a subset of containers are initially known and the departure times of other containers are revealed at once at a later time. The contributions are twofold. First, we propose an approximate stochastic optimization algorithm, called ASA*, which is a branch-and-bound framework combined with a sampling technique, and to the best of our knowledge is the first optimization algorithm proposed for this problem. We provide theoretical bounds on the approximation errors and present numerical results showing the computational tractability and efficiency of our algorithm. Second, we use the ASA* algorithm and a myopic heuristic to study the value of information, that is, the effect of the number of containers initially known on the number of relocations. In the last part of this thesis, we introduce a simulator that is capable of integrated simulation of port operations, including the retrieval process, the stacking process, and other aspects such as allocating cranes to containers and allocating trucks to cranes. Our simulator captures the practical details of operations that cannot be modelled in an optimization framework and is capable of simulating long periods (e.g. a week) of realistic-scale operations.
Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.; Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 107-109).
2015-01-01T00:00:00Z