Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. / Includes bibliographical references (p. 79-87). / This thesis explores how operations research methods can be applied in the emergency response community by looking at two recent tropical storm disasters; tropical cyclone Yemyin in Pakistan, June 2007 and super typhoon Durian in the Philippines, Nov 2006. The case studies are used to highlight three common problem areas; determining the scope of the disaster, agency coordination, and relief logistics. The thesis identifies some operational models and applicable research and suggests that these ideas should be formulated as emergency management decision making tools particularly for use in the developing world. / by Maurice D. Murphy. / S.M.

Thesis: S.M., 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 55-60). / We build on existing operations and marketing research focusing on the effect of information transparency on consumers by studying how transparency into the levels and changes of relative sustainability performance affects consumer behavior. Our work considers two forms of transparency: process transparency and customer transparency. We operationalize process transparency, in which information about the company's sustainability performance relative to competitors is revealed to the customer, in the product purchase domain. We operationalize customer transparency, in which the customer receives information about their own sustainability performance relative to other customers, in the energy consumption domain. In a series of online consumer choice experiments, we find that within the product purchase domain, transparency into the company's current levels of sustainability performance has a more powerful effect on influencing consumer purchase behavior than transparency into the company's changes in relative sustainability performance over time. Conversely, in the energy consumption domain, we find that transparency into the customer's changes in sustainability performance over time, relative to other customers, has a more dominant effect in motivating energy conservation than transparency into the customer's relative levels of sustainability performance. We employ structural equation models to identify the underlying mechanisms that drive these results. / by Shwetha Paramananda Mariadassou. / S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008. / Includes bibliographical references (p. 115-116). / We present in this work a unified approach and provide the optimal solution to the pricing problem of option contracts for a supplier of an industrial good in the presence of spot trading. Specifically, our approach fully and jointly endogenizes the determination of three major characteristics in contract design, namely (i) Sales contracts versus options contracts; (ii) Flat fee versus volume- dependent contracts; and (iii) Volume discounts versus volume premia; combining them together with spot market trading decisions and also the option of delaying production for the seller. We build a model where a supplier of an industrial good transacts with a manufacturer who uses the supplier's product to produce an end good with an uncertain demand. We derive the general non-linear pricing solution for the contracts under information asymmetry of the buyer's production flexibility. We show that confirming industry observations, volume-dependent optimal sales contracts always demonstrate volume discounts (i.e., involve concave pricing). On the other hand the options contracts are more complex agreements, and optimal contracts for them can involve both volume discounts and volume premia. Further, we find that in the optimal contracts, there are three major pricing regimes. First, if the seller has a higher discount rate than the buyer and the production costs are lower than a critical threshold value, the optimal contract is a flat fee sales contract. Second, when the seller is less patient than the buyer but production costs are higher than the critical threshold, the optimal contract is a sales contract with volume discounts. Third, if the buyer has a higher discount rate than the seller, then the optimal contract is a volume-dependent options contract and can involve both volume discounts and volume premia. We further provide links between industry and spot market characteristics, contract characteristics and efficiency. Last, we look into an extension of our basic model, where we give an analysis for the case when the seller is given a last minute production option. / by Pamela Pen-Erh Pei. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 137-140). / The cost of air traffic delays is well documented, and furthermore, it is known that the significant proportion of delays is incurred at airports. Much of the air traffic flow management literature focuses on traffic flows between airports in a network, and when studies have focused on optimizing airport operations, they have focused largely on a single aspect at a time. In this thesis, we fill an important gap in the literature by proposing unified approaches, on both strategic and tactical levels, to optimizing the traffic flowing through an airport. In particular, we consider the entirety of key problems faced at an airport: a) selecting a runway configuration sequence; b) determining the balance of arrivals and departures to be served; c) assigning flights to runways and determining their sequence; d) determining the gate-holding duration of departures and speedcontrol of arrivals; and e) routing flights to their assigned runway and onwards within the terminal area. In the first part, we propose an optimization approach to solve in a unified manner the strategic problems (a) and (b) above, which are addressed manually today, despite their importance. We extend the model to consider a group of neighboring airports where operations at different airports impact each other due to shared airspace. We then consider a more tactical, flight-by-flight, level of optimization, and present a novel approach to optimizing the entire Airport Operations Optimization Problem, made up of subproblems (a) - (e) above. Until present, these have been studied mainly in isolation, but we present a framework which is both unified and tractable, allowing the possibility of system-optimal solutions in a practical amount of time. Finally, we extend the models to consider the key uncertainties in a practical implementation of our methodologies, using robust and stochastic optimization. Notable uncertainties are the availability of runways for use, and flights' earliest possible touchdown/takeoff times. We then analyze the inherent trade-off between robustness and optimality. Computational experience using historic and manufactured datasets demonstrates that our approaches are computationally tractable in a practical sense, and could result in cost benefits of at least 10% over current practice. / by Michael Joseph Frankovich. / Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / "June 2013." Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 155-157). / collect information. This may include taking pictures of the ground, gathering infrared photos, taking atmospheric pressure measurements, or any conceivable form of data collection. Often these separate organizations have overlapping collection interests or flight plans that are sending sensors into similar regions. However, they tend to be controlled by separate planning systems which operate on asynchronous scheduling cycles. We present a method for coordinating various collection tasks between the planning systems in order to vastly increase the utility that can be gained from these assets. This method focuses on allocation of collection requests to scheduling systems rather than complete centralized planning over the entire system so that the current planning infrastructure can be maintained without changing any aspects of the schedulers. We expand on previous work in this area by inclusion of a learning method to capture information about the uncertainty pertaining to the completion of collection tasks, and subsequently utilize this information in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end. / by Eric John Robinson. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2001. / Includes bibliographical references (leaves 61-62). / We consider a single product multistage serial inventory system with several installations, say N - I, ... , l. Installation N - I intakes exogenous supply of a single commodity. For i E {I, ... N - 2}, installation i is supplied by shipments from installation i + 1. Demands for the finished good occur at installation l. Demands that cannot be filled immediately are backlogged. We assume holding costs at each installation which are linear functions of inventory, as well as a constant cost for each unit of backlogged demand, per period. Clark and Scarf {1960) showed that over a finite horizon an echelon basestock policy is optimal. Federgruen and Zipkin (1984) extend their result to the infinite-horizon case for both discounted and average costs. We present a new approach to this multistage serial inventory management problem, and give new proofs of these results by introducing and solving a simple Travel Time problem, using Dynamic Programming. This approach is motivated by the fact that the exact cost-to-go function of the related Travel Time problem can be easily computed using a straightforward recursive procedure (instead of using the typical value iteration or policy iteration methods). Moreover, this cost-to-go function gives various insights useful for a group of more complex multistage inventory problems. In this regard, we discuss how this cost-to-go function can be used to develop good Approximate Dynamic Programming algorithms for a number of complex multistage serial inventory problems. The results obtained suggest that the idea of introducing a related "Travel Time" problem and our algorithm to solve this problem can be used as a building block of a new approach to solve large scale multistage inventory management problems. This thesis was part of a research effort to find a fast algorithm to get very good robust suboptimal solutions to large scale multistage inventory management problems. / by Aristide C.E. Achy-Brou. / S.M.

Thesis: S.M., 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. / "June 2015." Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 85-90). / Developed in the 1970's and 1980's, the Traffic Alert and Collision Avoidance System (TCAS) is the last safety net to prevent an aircraft mid-air collision. Although TCAS has been historically very effective, TCAS logic must adapt to meet the new challenges of our increasingly busy modern airspace. Numerous studies have shown that formulating collision avoidance as a partially-observable Markov decision process (POMDP) can dramatically increase system performance. However, the POMDP formulation relies on a number of design parameters modifying these parameters can dramatically alter system behavior. Prior work tunes these design parameters with respect to a single performance metric. This thesis extends existing work to handle more than one performance metric. We introduce an algorithm for preference elicitation that allows the designer to meaningfully define a utility function. We also discuss and implement a genetic algorithm that can perform multi-objective optimization directly. By appropriately applying these two methods, we show that we are able to tune the POMDP design parameters more effectively than existing work. / by John R. Lepird. / 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 108-114). / In this thesis, we employ healthcare analytics to inform system-level changes at Massachusetts General Hospital that could lead to a significant reduction in avoidable hospitalization days and improvement in patients outcomes. The first area of focus is around avoidable bed-days in the ICU. Many surgical patients experience non-clinical delays when they transfer from the ICU to a subsequent general care unit where they are expected to continue their recovery. As a result, they spend a longer time in the ICU than necessary. In spite of several studies that suggest out-of-ICU transfer delays are quite common, there is little work that quantifies the impact on patient recovery. Using multiple statistical approaches including regression and matching, we obtain a robust result that suggests that non-clinical transfer delays from the ICU delay the patient's recovery as well as extend the hospital LOS. Specifically, the analysis shows that each day that the patient is delayed in the ICU for non-clinical reasons increases hospital LOS by 0.71 days (p-value < 0.01) and the patient's progress of care by 0.32 days (p-value < 0.01), on average. The second area of focus is concerned with bed-days from heart failure (HF) admissions. Much of the current work in reducing HF hospitalizations promotes interventions after the patient is hospitalized, aiming to prevent subsequent hospitalizations within 30 days. In contrast, we focus on reducing overall hospitalizations from the general HF population. We first analyze the outpatient access for these patients before they are admitted to the hospital (mostly) through the Emergency Department. One of the main findings is that in more than half of these admissions, the patient did not have a completed appointment with any outpatient clinic (Primary Care, Cardiology, or Home Health) during the two weeks prior to hospitalization. This reveals the need for improved outpatient-based preventative measures to manage HF patients. To partially address this challenge, we develop a predictive model using a logistic regression to predict the risk of a HF-related admission within the next six months. The model performs quite well with an out-of-sample AUC of 0.78. / by Jazmin D. Furtado. / 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 143-149). / The Last Mile Problem refers to the provision of travel service from the nearest public transportation node to a home or office. Last Mile Transportation Systems (LMTS) are critical extensions to traditional public transit systems. We study the LMTS from three perspectives. The first part of this thesis focuses on the design of a LMTS. We study the supply side of LMTS in a stochastic setting, with batch demands resulting from the arrival of groups of passengers at rail stations or bus stops who request last-mile service. Closed-form bounds and approximations are derived for the performance of LMTS as a function of the fundamental design parameters of such systems. It is shown that a particular strict upper bound and an approximate upper bound perform consistently and remarkably well. These expressions can therefore be used for the preliminary planning and design of Last Mile Transportation Systems. The second part of the thesis studies operating strategies for LMTS. Routes and schedules are determined for a multi-vehicle fleet of delivery vehicles with the objective of minimizing the waiting time and riding time of passengers. A myopic operating strategy is introduced first. Two more advanced operating strategies are then described, one based on a metaheuristic using tabu search and the other using an exact Mixed Integer Programming model, which is solved approximately in two stages. It is shown that all three operating strategies greatly outperform the naive strategy of fixed routes and fixed vehicle dispatching schedules. The third part presents a new perspective to the study of passenger utility functions in a LMTS. The unknown parameters of a passenger utility function are treated as unobserved events, and the characteristics of the transportation trips made by the passengers are treated as observed outcomes. We propose a method to identify the probability measures of the events given observations of the frequencies of outcomes by introducing the concept and assumptions of the Core Determining Class. We introduce a combinatorial algorithm in which the noise in the observations data is ignored and a general procedure in which data noise is taken into consideration. / by Hai Wang. / Ph. D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2001. / Includes bibliographical references (p. 153-154). / In this thesis, we create a comprehensive model and efficient solution technique for generating air operations plans. Previous air operations models have fallen short in at least one of the following areas: routing. real-time re-planning of aircraft. problem size capability, plan generation speed. and optimal packaging of aircraft. The purpose of the Composite Mission Variable Decomposition (CMVD) approach is to plan and re-plan air operations for a real conflict as it unfolds. Previous model shortcomings were the result of two main reasons: the models were developed for other purposes (typically weapons studies). or developers could not create techniques that can efficiently generate plans while including the listed areas. The application of conventional optimization modeling to an operations problem that includes aspects such as routing and real-time re-planning forms a model that has millions of constraints and a weak linear programming relaxation. The Composite Mission Variable MNodeils the first step in overcoming the above shortcomings because it greatly decreases the number of constraints in the optimization model. and the linear programming relaxation provides tight bounds. The Composite Mission Variable Model combines multiple air operations planning decisions into a composite mission variable. Many complex constraints that are explicitly included in a conventional model are implicitly enforced in the composite mission variables. We apply price coordinated decomposition to generate the composite mission variables. Price coordination reduces the number of variables in the Composite Mission Variable Model and allows for parallel processing of composite mission variable generation. CMVD creates air operations plans in minutes for scenarios with thousands of targets. while including important capabilities such as routing and re-planning of aircraft in air. CMVD is tested in simulated conflicts and its performance validated by comparisons with a heuristic approach for generating plans. / by Christopher D. Barth. / S.M.