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 student-submitted PDF version of thesis. / Includes bibliographical references (pages 75-80). / Black box machine learning methods have allowed researchers to design accurate models using large amounts of data at the cost of interpretability. Model interpretability not only improves user buy-in, but in many cases provides users with important information. Especially in the case of the classification problems addressed in this thesis, the ideal model should not only provide accurate predictions, but should also inform users of how features affect the results. My research goal is to solve real-world problems and compare how different classification models affect the outcomes and interpretability. To this end, this thesis is divided into two parts: food safety risk analysis and human trafficking detection. The first half analyzes the characteristics of supermarket suppliers in China that indicate a high risk of food safety violations. Contrary to expectations, supply chain dispersion, internal inspections, and quality certification systems are not found to be predictive of food safety risk in our data. The second half focuses on identifying human trafficking, specifically sex trafficking, advertisements hidden amongst online classified escort service advertisements. We propose a novel but interpretable keyword detection and modeling pipeline that is more accurate and actionable than current neural network approaches. The algorithms and applications presented in this thesis succeed in providing users with not just classifications but also the characteristics that indicate food safety risk and human trafficking ads. / by Jessica H. Zhu. / 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 145-151). / Resource optimization has always been a challenge both in traditional fields, such as logistics, and particularly so in most emerging systems in the sharing economy. These systems are by definition founded on the sharing of resources among users, which naturally creates many coordination needs as well as challenges to ensure enough resource supply to cover customer demand. This thesis addresses these challenges in the application of vehicle sharing systems, as well as in the context of multi-operation companies that provide a wide range of services to their users. More specifically, the first part of this thesis focuses on models and algorithms for the optimization of bike sharing systems. Shortage of bikes and docks is a common issue in bike sharing systems, and, to tackle this problem, operators use a fleet of vehicles to redistribute bikes across the network. / We study multiple aspects of these operations, and develop models that can capture all user trips that are performed successfully in the system, as well as algorithms that generate complete redistribution plans for the operators to maximize the served demand, in running times that are fast enough to allow real-time information to be taken into account. Furthermore, we propose an approach for the estimation of the actual user demand which takes into account both the lost demand (users that left the system due to lack of bikes or docks) and shifted demand (users that had to walk to nearby stations to find available resources). More accurate demand representations can then be used to inform better decisions for the daily operations, as well as the long-term planning of the system. The second part of this thesis is focused on schedule generation for resources of large companies that must support a complex set of operations. / Different operation types come with a variety of constraints and requirements that need to be taken into account. Moreover, specialized employees with a variety of skills and experience levels are required, along with an heterogeneous fleet of vehicles with various properties (e.g., refrigerator vehicles). We introduce the Complex Event Scheduling Problem (CESP), which captures known problems such as pickup-and-delivery and technician scheduling as special cases. We then develop a unified optimization framework for CESP, which relies on a combination of metaheuristics (ALNS) and Linear Programming. Our experiments show that our framework scales to large problem instances, and may help companies and organizations improve operation efficiency (e.g., reduce fleet size). / by Konstantina Mellou. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 141-145). / In the design and operation of urban mobility systems, it is often desirable to understand patterns in traveler demand. However, demand is typically unobserved and must be estimated from available data. To address this disconnect, we begin by proposing a method for recovering an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation technique based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information about the unknown distribution is available but the truncation process can be simulated. / The proposed method is compared to the ubiquitous maximum likelihood (MLE) method in a variety of synthetic validation experiments where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slight misspecified MLE. We then describe a novel car-sharing simulator which captures many of the important interactions between supply, demand, and system utilization while remaining simple and computationally efficient. In collaboration with Zipcar, a leading car-sharing operator in the United States, we demonstrate the usefulness of our detruncation method combined with our simulator via a pair of case studies. These tools allow us to estimate demand for round trip car-sharing services in the Boston and New York metropolitan areas, and the inferred demand distributions contain actionable insights. / Finally, we extend the detruncation method to cover cases where data is noisy, missing, or must be combined from different sources such as web or mobile applications. In synthetic validation experiments, the extended method is benchmarked against kernel density estimation (KDE) with Gaussian kernels. We find that the proposed method typically outperforms KDE, especially when the distribution to be estimated is not unimodal. With this extended method we consider the added utility of search data when estimating demand for car-sharing. / by Evan Fields. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (leaves 74-79). / In this thesis, we examine some puzzles in finance from an evolutionary perspective. We first provide a literature review of evolutionary psychology, and discuss three main findings; the frequentist hypothesis, applications from risk-sensitive optimal foraging theory, and the cheater detection hypothesis. Next we introduce some of the most-researched puzzles in the finance literature. Examples include overreaction, loss aversion, and the equity premium puzzle. Following this, we discuss risk-sensitive optimal foraging theory further and examine some of the financial puzzles using the framework of risk-sensitive foraging. Finally, we develop a dynamic patch selection model which gives the patch selection strategy that maximizes an organism's long-run probability of survival. It is from this optimal patch strategy that we observe loss aversion. Throughout the thesis, we stress the following: humans' behavior in financial markets is neither inherently irrational, nor is it rational. Rather the puzzles occur as a consequence of evolutionarily-optimal cognitive mechanisms being utilized in environments other than the ancestral domain in which they evolved to adapt in. / by Kenrick Guo. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (p. 205-209). / With the continued development of high endurance Unmanned Aerial Vehicles (UAV) and Unmanned Combat Aerial Vehicles (UCAV) that are capable of performing autonomous fiunctions across the spectrum of military operations, one can envision a future military in which Air Component Commanders control forces comprised exclusively of unmanned vehicles. In order to properly manage and fully realize the capabilities of this UAV force, a control system must be in place that directs UAVs to targets and coordinates missions in a manner that provides an efficient allocation of resources. Additionally, a mission planner should account for the uncertainty inherent in the operations. Uncertainty, or stochasticity, manifests itself in most operations known to man. In the battlefield, such unknowns are especially real; the phenomenon is known as the fog of war. A good planner should develop plans that provide an efficient allocation of resources and take advantage of the system's true potential, while still providing ample "robustness" ill plans so that they are more likely executable and for a longer period of time. / (cont.) In this research, we develop a UAV Mission Planner that couples the scheduling of tasks with the assignment of these tasks to UAVs, while maintaining the characteristics of longevity and efficiency in its plans. The planner is formulated as a Mixed Integer Program (MIP) that incorporates the Robust Optimization technique proposed by Bertsimas and Sim [12]. / by Philemon Sakamoto. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (p. 63-64). / We consider in this thesis the problem of pricing American Put Options in a model-free framework where we do not make any assumptions about the price dynamics of the underlying except those implied by the no-arbitrage conditions. Our goal is to obtain bounds on the price of an American put option with a given strike and maturity directly from the prices of other American put options with the same maturity but different strikes and the current price of the underlying. We proceed by first investigating the structural properties of the price curve of American Put Options of a fixed maturity and derive necessary and sufficient conditions that strike - price pairs of these options must satisfy in order to exclude arbitrage. Using these conditions, we can find tight bounds on the price of the option of interest by solving a very tractable Linear Programming Problem. We then apply the methods developed to real market data. We observe that the quality of bounds that we obtain compares well with the quoted bid-ask spreads in most cases. / by Premal Shah. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. / Includes bibliographical references (p. 81). / A large online retailer strategically stocks inventory for SKUs with low demand. The motivations are to provide a wide range of selections and faster customer fulfillment service. We assume the online retailer has the technological capability to manage and control the inventory globally: all warehouses act as one to serve the global demand simultaneously. The online retailer will utilize its entire inventory, regardless of location, to serve demand. We study inventory allocation and order fulfillment policies among warehouses for low-demand SKUs at an online retailer. Thus, given the global demand and an order fulfillment policy, there are tradeoffs involving inventory holding costs, transportation costs, and backordering costs in determining the optimal system inventory level and allocation of inventory to warehouses. For the case of Poisson demand and constant replenishment lead time, we develop methods to approximate the key system performance metrics like transshipment, backorders and average system inventory for one-for-one replenishment policies when warehouses hold exactly one unit of inventory. We run computational experiments to test the accuracy of the approximation. We develop extensions for cases when more than one unit of inventory is held at a warehouse. / (cont.) We then use these results to develop guidelines for inventory stocking and order fulfillment policies for online retailers. We also compare warehouse allocation policies for conditions when an order arrives but the preferred warehouse does not have stock although there is stock at more than one other location in the system. We develop intuition about the performance of these policies and run simulations to verify our hypotheses about these policies. / by Pallav Chhaochhria. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. / Includes bibliographical references (p. 92-93). / In this thesis, we analyze intratheater airlift operations, and propose methods to improve the planning process. The United States Air Mobility Command is responsible for the air component of the world wide U.S. military logistics network. Due to the current conflict in Iraq, a small cell within Air Mobility Command, known as Theater Direct Delivery, is responsible for supporting ongoing operations by assisting with intratheater airlift. We develop a mathematical programming approach to schedule airlift missions that pick up and deliver prioritized cargo within time windows. In our approach, we employ composite variables to represent entire missions and associated decisions, with each decision variable including information pertaining to the mission routing and scheduling, and assigned aircraft and cargo. We compare our optimization-based approach to one using a greedy heuristic that is representative of the current planning process. Using measures of efficiency and effectiveness, we evaluate and compare the performance of these different approaches. Finally, we adjust selected parameters of our model and measure the resulting changes in operating performance of our solutions, and the required computational effort to generate the solutions. / by Kiel M. Martin. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. / Includes bibliographical references (p. 129-134). / With recent advances in research and technology, autonomous surface vessel capabilities have steadily increased. These autonomous surface vessel technologies enable missions and tasks to be performed without the direction of human operators, and have changed the way scientists and engineers approach problems. Because these robotic devices can work without manned guidance, they can execute missions that are too difficult, dangerous, expensive, or tedious for human operators to attempt. The United States government is currently expanding the use of autonomous surface vessel technologies through the United States Navy's Spartan Scout unmanned surface vessel (USV) and NASA's Ocean-Atmosphere Sensor Integration System (OASIS) USV. These USVs are well-suited to complete monotonous, dangerous, and time-consuming missions. The USVs provide better performance, lower cost, and reduced risk to human life than manned systems. In this thesis, we explore how to plan multiple USV observation schedules for two significant notional observation scenarios, collecting water temperatures ahead of the path of a hurricane, and collecting fluorometer readings to observe and track a harmful algal bloom. / (cont.) A control system must be in place that coordinates a fleet of USVs to targets in an efficient manner. We develop three algorithms to solve the unmanned surface vehicle observation-planning problem. A greedy construction heuristic runs fastest, but produces suboptimal plans; a 3-phase algorithm which combines a greedy construction heuristic with an improvement phase and an insertion phase, requires more execution time, but generates significantly better plans; an optimal mixed integer programming algorithm produces optimal plans, but can only solve small problem instances. / by John V. Miller. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2007. / Includes bibliographical references (p. 227-229). / By combining the strengths of human and computers, Human Machine Collaborative Decision Making has been shown to generate higher quality solutions in less time than conventional computerized methods. In many cases, it is difficult to model continually changing problems and incorporate human objectives into the solution. Human-guided algorithms (HGAs) harness the power of sophisticated algorithms and computers to provide flexibility to the human decision maker to model correctly and dynamically the problem and steer the algorithm to solutions that match his/her objectives for the given problem. HGAs are designed to make the power of Operations Research accessible to problem domain experts and decision makers, and incorporate their expert knowledge into every solution. In order to appropriately utilize algorithms during a planner's decision making, HGA operators must appropriately trust the HGA and the final solution. Through the use of trust-based design (TBD), it was hypothesized that users of the HGA will gain better insight into the solution process, improve their calibration of trust, and generate superior solutions. The application of TBD requires the consideration of algorithms, solution steering methods, and displays required to best match human and computer complimentary strengths and to generate solutions that can be appropriately trusted. / (cont.) Abstract hierarchy, Ecological Interface Design, and various trust models are used to ensure that the HGA operators' evaluation of trust can be correctly calibrated to all necessary HGA trust attributes. A human-subject evaluation was used to test the effectiveness of the TBD design approach for HGAs. An HGA, including the appropriate controls and displays, was designed and developed using the described TBD approach. The participants were presented with the task of using the HGA to develop a routing plan for military aircraft to prosecute enemy targets. The results showed that TBD had a significant effect on trust, HGA performance, and in some cases the quality of final solutions. Another finding was that, HGA operators must be provided with additional trust related information to improve their understanding of the HGA, the solution process, and the final solution in order to calibrate properly their trust in the system. / by Joseph L. Thomer. / S.M.