Performance-based engineering and life-cycle management of infrastructure and technology systems
Mathematical modeling and control theory for life-cycle management optimization of sustainable infrastructure and environment systems–technology, system analytics, and decision science.
In this research, mathematical and probabilistic models, hybrid numerical approaches, computational algorithms, and integrated software platforms for modeling and managing the interactions among interdependent urban infrastructure networks (e.g., infrastructure facility and operations management, fleet asset management and maintenance under environmental regulations, etc.) are created. We developed a multi-objective infrastructure systems life-cycle optimization model using stochastic dynamic programming and evolutionary algorithms. The study developed a comprehensive framework of life cycle optimization for large infrastructure systems that help determine optimal multistage maintenance actions and schedules with multi-criteria objectives. The complex optimization problem is solved by a decomposed two-phase approach. At the project level, an integrated method that combines stochastic duration model with dynamic programming is developed to identify the optimal action for individual infrastructure. At the system-level, the problem is formulated by combinatorial optimization with multiple objectives and multiple constraints. The study captures the stochastic property of infrastructure deterioration, provides the optimal strategies to allocate the limited budget, examines the trade-off between economic costs and emissions, and demonstrates the importance of adopting a life-cycle approach. We also examined a pair of models designed to assist in the management of multiple deteriorating real assets, given financial and environmental concerns. Whether the assets are buildings or vehicles or machines, their purchase and upkeep can be costly, making optimal management policies valuable. The models incorporate numerous factors which have been modeled previously, though generally not together. These include technological change, linked decisions for multiple assets, and non-steady-state demand. They stand out from previous literature due to their ability to model retrofits, as well as repairs and replacements. These retrofits can have initial as well as ongoing costs, and can impact externalities, making them relatively general. The integer program model is fast and well suited to analysis requiring large numbers of runs, such as the comparison of a wide range of regulatory alternatives. The approximate dynamic program, while slower, is able to handle stochastic asset failures and repair costs for large asset portfolios, something which previous models have struggled to accomplish without strong simplifying assumptions.