Optimal control strategies for stochastic networks with multiple decision makers

Abstract

Decision makers often confront an inability to understand the consequences of interactions within systems of systems (SoS), which can have physical and human components and exhibit hybrid (continuous and discrete) dynamics. The human and physical interactions with the environment and related uncertainties can make optimization and control difficult and result in unintended consequences. As examples, transportation networks may experience lengthy delays or gridlock, and economic stimuli may be ineffective, as a result of suboptimal network control policies. The objective of this dissertation is to motivate, propose and implement a framework that provides decision support in order to manage and operate human-physical networks with hybrid dynamics. The stochastic human-physical analysis framework facilitates the integration of system simulation, uncertainty analysis and optimization under uncertainty for this class of problems. Specifically, this dissertation: 1) motivates the necessity for a SoS approach to optimizing network control policy; 2) proposes a SoS approach to policy analysis and design under uncertainty; 3) develops an integrated discrete choice and agent-based simulation approach for stochastic human-physical networks with hybrid dynamics; 4) develops and validates computationally inexpensive surrogate models to predict high-fidelity simulation outputs, and uses these models to perform probabilistic reachability analysis and sensitivity analysis; and 5) performs uncertainty propagation and stochastic policy optimization considering both cooperative and non-cooperative decision-makers.