A framework for the long-term planning of infrastructure based on flexibility (2024)
This dissertation presents a framework for the long-term planning of infrastructure subject to conditions of deep uncertainty based on the concept of flexibility. The proposed framework combines ideas and methodologies from two fields that have evolved mostly separately from each other: decision-making under deep uncertainty (DMDU) and flexibility analysis. Concretely, existing DMDU methods do not consider technical options for the adaptation of infrastructure systems, while the complex issue of deep uncertainty has been overlooked in most flexibility analysis approaches. This work aims to address this gap by presenting a design framework that explicitly considers technical options that enable adaptations to the system and tests their performance under a diverse set of external conditions to account for the deep uncertainty ever present in long-term planning.  In particular, the proposed framework combines exploratory modeling with reinforcement learning to find policies that are robust over many different futures. The framework also introduces a quantitative measure of flexibility that characterizes the adaptation potential using a vector in $\mathbb{R}^{2}$. Combined with the results from the exploratory modeling simulations, this representation can be used to determine the value provided by flexibility. In addition to the technical options that enable flexibility, the framework also considers different policies that control the adaptation of the system depending on the evolution of the uncertain conditions and the stakeholders' preferences.  The framework utilizes multiple models for the system and the environment to consider both internal and external uncertainty. Using a scenario generator, it simulates the interaction between the system and several external conditions to produce various economic and technical outcomes. Based on these outcomes, potential designs and policies are identified; then, using scenario discovery methods, it finds subsets of environmental conditions that cause these policies to fail. Next, it uses reinforcement learning to improve upon these policies, first under the challenging environments and later under the full ensemble of conditions. The purpose is to understand the limitations of the options as well as find key features that could be used to devise new and more robust policies and designs.  Two examples are used to demonstrate the framework. The first case considers a sea-dike under uncertainty of rising sea level. The objective is to find the design and adaptation policies that fulfill strict safety criteria while keeping costs low. Several flexible and non-flexible design options are analyzed; each flexible option is represented by an initial and a maximum height, while the non-flexible options keep their particular size constant. The results show that the staggered expansion of the dike can provide economic benefits without affecting the reliability of the structure.