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A Multi-Objective No-Regret Decision Making Model With Bayesian Learning For Autonomous Unmanned Systems, Matthew Howard
A Multi-Objective No-Regret Decision Making Model With Bayesian Learning For Autonomous Unmanned Systems, Matthew Howard
Electronic Theses and Dissertations
The development of a multi-objective decision making and learning model for the use in unmanned systems is the focus of this project. Starting with traditional game theory and psychological learning theories developed in the past, a new model for machine learning is developed. This model incorporates a no-regret decision making model with a Bayesian learning process which has the ability to adapt to errors found in preconceived costs associated with each objective. This learning ability is what sets this model apart from many others. By creating a model based on previously developed human learning models, hundreds of years of experience …
Improving Airline Schedule Reliability Using A Strategic Multi-Objective Runway Slot Assignment Search Heuristic, Florian Hafner
Improving Airline Schedule Reliability Using A Strategic Multi-Objective Runway Slot Assignment Search Heuristic, Florian Hafner
Electronic Theses and Dissertations
Improving the predictability of airline schedules in the National Airspace System (NAS) has been a constant endeavor, particularly as system delays grow with ever-increasing demand. Airline schedules need to be resistant to perturbations in the system including Ground Delay Programs (GDPs) and inclement weather. The strategic search heuristic proposed in this dissertation significantly improves airline schedule reliability by assigning airport departure and arrival slots to each flight in the schedule across a network of airports. This is performed using a multi-objective optimization approach that is primarily based on historical flight and taxi times but also includes certain airline, airport, and …