Optimizing Flexible Complex Systems with Coupled and Co-Evolving Subsystems under Operational Uncertainties

TL;DR

Problem: designing and deploying large-scale, interdependent infrastructure under operational uncertainties is computationally hard when using fully flexible stochastic formulations. The paper proposes a local scenario discretization approach that quantifies dynamic coupling to allocate scenarios per subsystem, decoupling weak interactions while preserving essential dependencies. It demonstrates that the approach yields near-full-flexible solutions with substantial computational savings across illustrative, random, and water–energy–food nexus cases, and discusses extensions to system partitioning and bottom-up built-in flexibility design. The framework enables scalable, flexible design of water, energy, food, and other critical infrastructure networks under uncertainty.

Abstract

The paper develops a novel design optimization framework and associated computational techniques for staged deployment optimization of complex systems under operational uncertainties. It proposes a local scenario discretization method that offers a computationally efficient approach to optimize staged co-deployment of multiple coupled subsystems by decoupling weak dynamic interaction among subsystems. The proposed method is applied to case studies and is demonstrated to provide an effective and scalable strategy to determine the optimal and flexible systems design under uncertainty. The developed optimization framework is expected to improve the staged deployment design of various complex engineering systems, such as water, energy, food, and other infrastructure systems.

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