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Daniel Guggenheim School of Aerospace Engineering

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Author: Andriano, Nelson Gregory ×

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    System of Systems Stakeholder Planning in a Multi-Stakeholder, Multi-Objective, and Uncertain Environment
    (Georgia Institute of Technology, 2021-07-29) Andriano, Nelson Gregory
    The United States defense planning process is currently conducted in a partially consolidated manner driven by the Joint Capabilities Integration and Development System (JCIDS) process. Decisions to invest in technology, develop systems, and acquire assets are made by individual services with coordination at the higher joint level. These individual service’s decisions are made in an environment where resource allocation and need are influenced by external stakeholders (e.g. shared system development costs, additional levied requirements, and complementary system development). The future outcome of any given decision is subject to a high degree of uncertainty stemming from both the stakeholder execution of a decision and the environment in which that execution will take place. Uncertainty in execution stems from TRL advancement, development timelines, acquisition timelines, and final deployed performance. Environmental uncertainty factors include future stakeholder resource availability, the future threat environment, cooperative stakeholder decisions, and mirrored adversary decisions. The defense planning problem can be described as an acknowledged System of Systems (SoS) planning problem. Today, methodologies exist that individually address SoS Engineering processes, the evaluation of SoS performance, and SoS system deterministic evolution. However, few approaches holistically address the SoS planning and evolution problem at the level needed to assist individual defense stakeholders in strategic planning. Current approaches do not address the impact of multiple-stakeholder decisions, multiple goals for each stakeholder, the uncertainty of decision outcomes, and the temporal component to strategic decision making. This thesis develops and tests a methodology to address defense stakeholder planning in a multi-stakeholder, multi-objective, and uncertain environment. First, a decision space is populated and captured via sampling a game framework that represents multiple stakeholder decisions as well as decision outcomes over time. A compressed Markov Decision Process (MDP) based meta-model is constructed using state-space consolidation techniques. The meta-model is evaluated using a risk-based policy development algorithm derived from combining traditional Reinforcement Learning (RL) techniques with mean-variance portfolio theory. Policy sensitivity to stakeholder risk-tolerance levels is used to develop state-based risk-tolerance sensitivity profiles and identify Pareto efficient actions. The risk-tolerance sensitivity profiles are used to evaluate both state spaces and decision spaces to provide stakeholders with risk-based insights, or rule sets, to support immediate decision making and risk-based stakeholder playbook development. The capability of the risk-based policy algorithm is tested using both elementary and complex scenarios. It is demonstrated that the algorithm can be used to extract Pareto efficient decisions as a function of risk-tolerance. The state space compression is tested via the comparison of the loss of information between the risk-based policy solutions for uncompressed and compressed state space. The full methodology is then demonstrated using a full-complexity scenario based on the joint development by France, Germany, and Spain of the SoS based Future Combat Air System (FCAS). The full complexity scenario is used to baseline the risk-based methodology against current optimal policy solution techniques. A significant increase in resulting derived insights relative to optimal policy solutions in a high uncertainty scenario is demonstrated.
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    Development and application of a rapid military model development framework
    (Georgia Institute of Technology, 2010-12-20) Andriano, Nelson Gregory
    Military operations are complex systems composed of the interactions of many smaller discrete systems, or assets: aircraft, watercraft, troops, etc. Historically, the requirements for new assets have been created based on standalone optimization. It is not just necessary to optimize requirements for a single scenario, such as a wartime operation, but instead to optimize the requirements that will benefit the entire military operation as a whole in a number of different scenarios, such as wartime and peace time. To better define future military assets it is necessary sample a large number of scenarios. To capture all of the interactions and develop a complete understanding of the overall system, it is necessary to model both combat and logistics, which have traditionally been modeled and analyzed separately. To characterize military operations and the assets that contribute to them, it is necessary to move beyond the traditional models that use aggregated approximations for combat and stand alone nodal analysis for logistics. A unique need for a framework which captures the complex interaction between combat and logistics while allowing a large number of automated cases and scenarios to run with no human in the loop. The framework this paper discusses was created to facilitate the making of models to analyze and characterize military operations and the effects that future assets will have on entire operations. The framework is agent-based, allowing bottom up definition and the gathering of emergent behavior, and uses a modified Hughes salvo method for combat, the Foundation for Intelligent Physical Agents messaging structure, and the beliefs, desires, and intentions (BDI) agent model. The modeling of communication and BDI creates myopic agents that are constrained by the information they can obtain, process, and react to. In this paper, the framework is first depicted and then validated by the creation of a model with the purposes of defining the requirements for a future asset, the Transformable Craft. The creation and testing of the model prove that the requirements for the framework have been met with success. The potential applications of the framework ranges from data-farming military operations models for future asset requirement, characterizing military operations systems, and providing a stepping stone for future agent-based military operations modeling and simulation work.