Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 177-185). / This work considers the nonlethal targeting assignment problem in counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We developed three models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counter-insurgents, 2) the network generation model, to arrive at a reasonable representation of a Pashtun district-level, opinion leader social network, and 3) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies the k US agent assignment strategy producing the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in experiments the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network. / by Benjamin W. K. Hung. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004. / Includes bibliographical references (p. 125-128). / Most of the logistics systems involve a multi-level distribution system structure due to value added by a multi-level configuration. Interactions of these levels, i.e. echelons, should be considered while making strategic decisions regarding the choice of the size, number and location of stocking sites as well as the tactical decision regarding the choice of inventory policy to be used. We analyze a two-echelon distribution network to characterize the market segmentation of each echelon and inventory deployment between the two-levels. Allocation of stock under a stochastic demand structure is considered simultaneously with warehousing and transportation decisions, which is an extension of the General Optimal Market Area (GOMA) Model developed by Erlenkotter. The distribution of inventory is investigated under different stock policies and the sensitivity of this distribution to various system parameters is analyzed. / by Nebibe Varol. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004. / Includes bibliographical references (p. 105-110). / There is a great need among educators for a way to quickly assign teams in large or distance learning classrooms in a manner superior to random assignment or student self-selection. Forming teams based on knowledge of students' characteristics is too time-consuming for large classrooms, yet research has shown that the characteristics of individuals greatly affect the quality of the teamwork experience. This thesis provides an automated method to quickly assign students to teams based on individual characteristics. We begin with a thorough review of the literature on how individuals' characteristics affect team behavior, focusing on the level of diversity of four main classes of traits - knowledge/skills/abilities, demographics, personality, and motivation. By forming teams that have diversity on some of these traits and homogeneity on others, we will be able to improve performance over randomly assigned teams. We frame this problem from a group dynamics perspective, measuring the compatibility of every dyad of students within a team. We propose, for several group environments, which traits should be homogeneous and which heterogeneous, and how important each trait is, and use these values to create an equation for a student compatibility score, a number representing how well a pair of students will work together. We then simulate team assignment to determine which of several heuristics is most efficient. A combination of random generation and pairwise exchange is found to be the best, forming teams with average compatibilities 307% higher than the average randomly generated team. The code for this program is included in the appendices. / (cont.) Additionally, we perform a classroom experiment in which sections of a class are divided into teams by three different methods - random assignment, intuition, and the method devised above. Although the experimental design was flawed, the results were encouraging, demonstrating that average student compatibility on a team was significantly positively associated with both the resulting team grade and the students' perception of how much they learned about teamwork. For a more detailed executive summary of this work, please see the Structure of the Thesis section on page 16. / by Kendell M. Timmers. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004. / Includes bibliographical references (p. 113-114). / This thesis describes several directions to replace the gradient in James Schor's gradient algorithm to solve the dual problem. The alternative directions are: the variance and standard deviation of buffer levels, the deviation of the average buffer level from half-full, the difference between probability of starvation and blocking, and the difference between the production rate upstream and downstream of a buffer. The objective of the new algorithms is to achieve the final production rate of the dual problem faster than the gradient. The decomposition method is used to evaluate the production rates and average buffer levels. We find that the new algorithms work better in lines with no bottlenecks. The variance and standard deviation algorithms work very well in most cases. We also include an algorithm to generate realistic line parameters. This algorithm generate realistic line parameters based on realistic constraints set on them. This algorithm does not involve filtering and is fast and reliable. / by Ketty Tanizar. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005. / Includes bibliographical references (p. 75-79). / We study the transient dynamics of a partially observed, infinite server queue fed with a Poisson arrival process whose controlled rate is changed at discrete points in time. More specifically, we define a state that incorporates partial information from the history of the process and write analytical formula for the dynamics of the system (state transition probabilities). Moreover, we develop an approximation method that makes the state finite-dimensional, and introduce techniques to further reduce the dimension of the state. This method could thus enable the formulation of tractable DPs in the future. / by Theophane Weber. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006. / Includes bibliographical references (p. 121-123). / We consider the problem of assigning a scarce number of interceptors to a wave of incoming atmospheric re-entry vehicles (RV). In this single wave, there is time to assign interceptors to a wave of incoming RVs, gain information on the intercept status, and then if necessary, assign interceptors once more. However, the status information of these RVs may not be reliable. This problem becomes challenging when considering the small inventory of interceptors, imperfect information from sensors, and the possibility of future waves of RVs. This work formulates the problem as a partially observable Markov decision process (POMDP) in order to account for the uncertainty in information. We use a POMDP solution algorithm to find an optimal policy for assigning interceptors to RVs in a single wave. From there, three cases are compared in a simulation of a single wave. These cases are perfect information from sensors; imperfect information from sensors, but acting as it were perfect; and accounting for imperfect information from sensors using the POMDP formulation. Using a variety of parameter variation tests, we examine the performance of the POMDP formulation by comparing the probability of an incoming RV avoiding intercept and the interceptor inventory remaining. We vary the reliability of the sensors, as well as the number of interceptors in inventory, and the number of incoming RVs in the wave. The POMDP formulation consistently provides a policy that conserves more interceptors and approaches the probability of intercept of the other cases. However, situations do exist where the POMDP formulation produces a policy that performs less effectively than a strategy assuming perfect information. / by Daniel B. McAllister. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009. / 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 (p. 225-226). / Improvised Explosive Devices, or IEDs, have become a familiar and lethal part of contemporary military operations in Iraq and Afghanistan, producing more casualties than any other weapons system. One reason for their success is their practicality in an environment characterized by imbalances in the capabilities of opposing forces. The military forces conducting stability operations in Iraq and Afghanistan rely on the existing road networks to support logistical and operational movements. Insurgents with limited firepower and maneuver capabilities can place a bomb on the side of a road and detonate it anonymously to cause catastrophic effects on a passing convoy. Route clearance teams were developed to combat the emerging threat of IEDs. Capable of detecting IEDs with minimal risk to troops, route clearance teams move along the road network in search of these destructive devices. This thesis explores a mathematical approach to planning and scheduling route clearance missions. To achieve this objective, we first develop a probability-based model of IED activities on a road network used by occupation forces. We then use approximate dynamic programming methods to generate potential route clearance missions that are effective at reducing the risk of IED attacks. Once the paths are generated, they are inputted into a mixed integer program that finds the most risk-reducing combination of missions that can feasibly be executed, given constraints on the availability of route clearance teams. / (cont.) A route clearance schedule and its associated risk-reduction metrics result. We conduct several experiments on the methods developed to test its validity and applicability. Our first experiment examines the effects of mission timing on IED risk reduction, and shows the difficulty in relating this timing to our knowledge of IED risk in the road network. The second experiment demonstrates the trade-offs associated with assigning different sectors of the road network to different route clearance teams versus assigning all teams to the entire network. Our last experiment confirms the value of having convoy and patrol schedules available when conducting route clearance planning. We conclude that the planning method developed, integrated with a graphical control interface, would provide a useful decision support tool for military planners scheduling route clearance operations. / by Christopher E. Marks. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005. / Includes bibliographical references (p. 43). / Given the desire to create interactive websites that effectively engage and instruct medical professionals, an alternative model for online case studies was developed. The resulting application presents the user with a virtual patient, asks for information regarding the treatment and care of that patient, and provides customized feedback to the user. When a person uses this application, one could say the goal of the user is to make the necessary decisions that will stabilize the patient, and the goal of the application is to provide feedback regarding those decisions. In order to adapt to user decisions, the design incorporates an unconventional use of decision analysis. The source of uncertainty is the clinician's strategy, or sequence of decisions. Given the user's decision, the appropriate system response is assumed to be uncertain a priori. The proposed model requires the application to conduct an internal analysis, and then condition the response on the circumstances under which the decision is made. This conditioning approach informs the patient's behavior during the simulation, and it determines the appropriate constructive feedback for the user. Intuitively, a system constructed using the proposed model is better suited to address the educational needs of an individual learner. Also, despite the context of this model, it is noted that the proposed model need not be restricted to medical applications. / by Lincoln J. Chandler. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2005. / Includes bibliographical references (p. 144-146). / Autonomous vehicles often operate in environments with imperfect information. This thesis addresses the case of a system of autonomous vehicles and sensors attempting to intercept a moving object of interest that arrives stochastically and moves stochastically after arrival. A sensor array is placed in the area of expected arrivals. As the object of interest moves across the sensor system, the system initially receives perfect information of the object's movements. After the object of interest leaves the sensor system, the algorithm uses statistical estimation techniques to develop confidence intervals about points of expected interception. The algorithm assigns the optimal, autonomous chase vehicle from a set of pre-positioned autonomous vehicles, develops movement commands for the assigned vehicle, and considers reassignment of chase vehicles as appropriate given the stochastic movements of the object of interest. Dynamic programming is employed to optimize system parameters, and the thesis considers a reformulation of the problem that uses dynamic programming as a structural model for the entire algorithm. / by Randal E. Hickman. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, May, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 111-125). / The Vehicle Routing Problem with Time Windows (VRPTW) has been widely studied in the Operations Research (OR) literature given its increasingly widespread applications, ranging from school bus scheduling to packages delivery. In the last decades, and in large part due to the surge in e-commerce and shortened promised lead times, the scale of the highly constrained VRPTW instances encountered in real-world applications has significantly increased. Simultaneously, various Machine Learning (ML) methods have been developed to tackle combinatorial problems and to leverage complex data structure, but little research has been done on applying these techniques to the VRPTW. In light of this research gap, our thesis develops a process to solve large-scale VRPTW without classical OR routing by proposing a two-stage algorithm. In the first stage, we design a clustering algorithm leveraging Optimal Classification Trees (OCT), which aims at dividing customers into smaller subsets. In the second stage, we present an actor-critic Reinforcement Learning (RL) approach to solve the VRPTW on these smaller customers clusters. Subsequently, we explore the interactions between ML and OR and develop a framework to overcome the difficulties linked to the differences between the train and test sets, as well as the adversity created by the OR algorithm. We also study the generalization limitations of RL methods. Results show that the clustering approach is competitive with regards to a k-means-based clustering, yielding improvements up to 5% in terms of number of vehicles, and that a RL approach can successfully solve medium-size VRPTW instances, providing optimality results similar to state-of-the-art industrial solvers. / by Julie Poullet. / S.M. / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center