https://hdl.handle.net/1721.1/7582 2022-01-11T23:30:47Z 2022-01-11T23:30:47Z Vigil, Shane J. https://hdl.handle.net/1721.1/138870 2022-01-11T03:06:14Z 2021-01-01T00:00:00Z

Automating Flow of a Material Handling System Vigil, Shane J. Amazon uses a system of interconnected manned work processing stations linked by conveyances that route items from different parts of the warehouse into a single order for packing. This thesis will examine one such system, which circulates items throughout the system in trays. Leaders manage and tune the production rate by adjusting the number of trays within the system to maximize throughput. This task takes considerable time and requires operators to manually add and remove trays from the system. To reduce time spent by leaders in managing trays, automated solutions are investigated. It is determined that the optimal number of trays within the system is dynamic. Furthermore, physical constraints of the system prevent an automated solution that simply inserts and removes trays based off an algorithm. This study uncovers unrealized throughput by creating a model of the system that outputs the ideal tray count based off historical data and mathematical constraints. Additionally, this thesis explores an automated solution that supplies and removes trays based off localized blockage and starvation. A Work Domain Analysis and a human factors study laid the foundations for automation. Simulation demonstrates the potential for a 21.2% production rate increase and a release of 7.14 hours/day for other tasks. Implementation of the model with an alert system increases throughput 20% during maximum production with a median error of 8.11% when targeting a desired throughput. These techniques can be extended to other circulation systems in manufacturing. As Industry 4.0 grows, the management of human-machine relations becomes critical for safety and performance.; A Work Domain Analysis and a human factors study laid the foundations for automation. Simulation demonstrates the potential for a 21.2% production rate increase and a release of 7.14 hours/day for other tasks. Implementation of the model with an alert system increases throughput 20% during maximum production with a median error of 8.11% when targeting a desired throughput. These techniques can be extended to other circulation systems in manufacturing. As Industry 4.0 grows, the management of human-machine relations becomes critical for safety and performance. Thesis: M.B.A. Massachusetts Institute of Technology, Sloan School of Management, June, 2021; Cataloged from the official PDF version of thesis.; Includes bibliographical references (pages 71-73).

2021-01-01T00:00:00Z Roberts, Thomas González https://hdl.handle.net/1721.1/138773 2022-01-04T03:24:58Z 2021-01-01T00:00:00Z

Geosynchronous Satellite Maneuver Classification and Orbital Pattern Anomaly Detection via Supervised Machine Learning Roberts, Thomas González Due to the nature of the geosynchronous (GEO) orbital regime, where space objects orbit the Earth once per sidereal day, GEO satellites can appear fixed to a position in the sky when observed from the Earth’s surface. This unique orbital characteristic makes GEO satellites ideal for telecommunications missions that require Earth-fixed antennas to send and receive signals, such as television broadcasts or military communications. To maintain their position relative to the Earth’s surface, GEO satellites must station-keep, or regularly expend onboard propellant to counteract the natural forces in the near-Earth space environment that perturb their orbital trajectories. Less frequently, GEO satellites perform maneuvers to alter their orbital characteristics more drastically. One such maneuver is a longitudinal shift: changing a GEO satellite’s sub-satellite point from one position on the Earth’s equator to another. Such a maneuver often requires both a series of impulsive thrusts and a period of natural drift. This work describes an approach for detecting the components of longitudinal shift maneuvers—including the patterns associated with initiating and ending eastward and westward drifts—using convolutional neural networks trained on publicly available two-line element (TLE) data from the U.S. Space Command’s (SPACECOM) space object catalog. A method for converting TLE data to geographic position histories—longitude, latitude, and altitude positions over time in the Earth-centered, Earth-fixed geographic reference frame—and labeling longitudinal shift maneuvers by inspection is described. A preliminary maneuver detection algorithm is designed, trained, and tested on all GEO satellites in orbit from January 1 to December 31, 2020. Performance metrics are presented for algorithms trained on two different training data sets corresponding to five and ten years’ worth of geographic position time-histories labeled with longitudinal shift maneuvers. When detected, longitudinal shift maneuvers can be used to identify anomalous behavior in GEO. In this work, a satellite’s behavior is considered nominal if it adheres to the satellite’s pattern of life (PoL)—its previous on-orbit behavior made up of sequences of both natural and non-natural behavioral modes, including routine station-keeping, other on-orbit maneuvers, and uncontrolled motion—and anomalous if it deviates from the satellite’s PoL. Identifying anomalous satellite behavior is of critical interest to space situational awareness (SSA) system operators, who may choose to task their sensors to obtain more observations of anomalous behavior, and satellite operators themselves, who may wish to diagnose its root cause. Applications of this work for international space policymaking, including the development of on-orbit norms of behavior and the distribution of spectral and physical space in GEO, is also discussed.; This work describes an approach for detecting the components of longitudinal shift maneuvers—including the patterns associated with initiating and ending eastward and westward drifts—using convolutional neural networks trained on publicly available two-line element (TLE) data from the U.S. Space Command’s (SPACECOM) space object catalog. A method for converting TLE data to geographic position histories—longitude, latitude, and altitude positions over time in the Earth-centered, Earth-fixed geographic reference frame—and labeling longitudinal shift maneuvers by inspection is described. A preliminary maneuver detection algorithm is designed, trained, and tested on all GEO satellites in orbit from January 1 to December 31, 2020. Performance metrics are presented for algorithms trained on two different training data sets corresponding to five and ten years’ worth of geographic position time-histories labeled with longitudinal shift maneuvers.; When detected, longitudinal shift maneuvers can be used to identify anomalous behavior in GEO. In this work, a satellite’s behavior is considered nominal if it adheres to the satellite’s pattern of life (PoL)—its previous on-orbit behavior made up of sequences of both natural and non-natural behavioral modes, including routine station-keeping, other on-orbit maneuvers, and uncontrolled motion—and anomalous if it deviates from the satellite’s PoL. Identifying anomalous satellite behavior is of critical interest to space situational awareness (SSA) system operators, who may choose to task their sensors to obtain more observations of anomalous behavior, and satellite operators themselves, who may wish to diagnose its root cause. Applications of this work for international space policymaking, including the development of on-orbit norms of behavior and the distribution of spectral and physical space in GEO, is also discussed. Thesis: S.M. in Technology and Policy, Massachusetts Institute of Technology, Institute for Data, Systems, and Society, Technology and Policy Program, June, 2021; Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, June, 2021; Cataloged from the official PDF of thesis.; Includes bibliographical references (pages 75-79).

2021-01-01T00:00:00Z Chevallier, Juliette https://hdl.handle.net/1721.1/138771 2021-12-23T15:33:17Z 2021-01-01T00:00:00Z

Enabling Autonomy in Commercial Aviation: An Ontology and Framework for Automating Unmanned Aircraft Systems (UAS) Chevallier, Juliette Autonomous air vehicles are rapidly gaining interest within the aviation industry with novel business cases such as urban air mobility, package delivery, and more. However, these increasingly autonomous systems come with increasingly numerous and complex inputs that software must handle. This limitless set of inputs must ensure that autonomous system decisions will translate to operations that are safe for the general public. This thesis contributes to knowledge by introducing an ontology and framework, with supporting analyses, to align individuals before beginning research and product development efforts in autonomous vehicles. This framework and the supporting ontology and analyses seek to provide a quantitative, repeatable method for describing the increase of operational uncertainty with the increase in automation for a UAS. Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, June, 2021; Thesis: M.B.A., Massachusetts Institute of Technology, Department of SLOAN, June, 2021; Cataloged from the official PDF of thesis.; Includes bibliographical references (pages 258-261).

2021-01-01T00:00:00Z Matthews, William Henry, 1942- https://hdl.handle.net/1721.1/138750 2021-12-21T03:17:20Z 1970-01-01T00:00:00Z

Development of a mechanism for utilizing science in global environmental policy formulation Matthews, William Henry, 1942- Thesis: Ph. D., Massachusetts Institute of Technology, Department of Political Science and the Sloan School of Management, Interdepartmental Program, Socio-Technological Engineeringg, 1970; Vita.; Includes bibliographical references.

1970-01-01T00:00:00Z