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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj
Multi-Stage Stochastic Optimization And Reinforcement Learning For Forestry Epidemic And Covid-19 Control Planning, Sabah Bushaj
Dissertations
This dissertation focuses on developing new modeling and solution approaches based on multi-stage stochastic programming and reinforcement learning for tackling biological invasions in forests and human populations. Emerald Ash Borer (EAB) is the nemesis of ash trees. This research introduces a multi-stage stochastic mixed-integer programming model to assist forest agencies in managing emerald ash borer insects throughout the U.S. and maximize the public benets of preserving healthy ash trees. This work is then extended to present the first risk-averse multi-stage stochastic mixed-integer program in the invasive species management literature to account for extreme events. Significant computational achievements are obtained using …
Reducing Uncertainty In Effects-Based Operations, Wilburn B. Mclamb
Reducing Uncertainty In Effects-Based Operations, Wilburn B. Mclamb
Theses and Dissertations
Known as the fog of war, uncertainty has been prevalent in the conduct of military operations throughout human history. Intelligence collection efforts are tasked to reduce this uncertainty through the collection of information. Utilizing Shannon's entropy as a measure of the expected information gain due to an intelligence collection effort, a methodology is developed to prioritize and allocate intelligence assets in an efficient manner. Incorporated in this methodology are target priority and the requirement to reassess dynamic targets. The application area for the methodology is Effects-Based Operations. A generalized state model is developed to conduct adversary system-of-systems analysis. This model …