An approach for analyzing and managing flexibility in engineering systems design based on decision rules and multistage stochastic programming

Techno‐economic planning of local energy systems through optimization models: a survey of current methods

“…In a second step, they formulate a multi‐stage stochastic MILP to optimize the investment decisions timing using the pre‐computed operational costs. The RO thinking is also applied in Reference 123*. Here again, the investment decisions timing is optimized.…”

A research agenda on systems approaches to infrastructure

“…Engineering and design data are increasingly generated and accumulated in infrastructure operations, project planning, and delivery. Examples of our work that uses this increasingly rich data extensively includes work to develop data, tools and methods to model the physical economy (Myers et al 2018;Myers, Reck, and Graedel 2019), to create more flexibility in design (Cardin et al 2017), to the operation of water systems (Nerantzis, Pecci, and Stoianov 2020) and internet of things (e.g. Benkhelifa et al 2020).…”

“…For these properties to be impactful, they need to be embedded in the early design phases through careful engineering technology, so as to extract better value in future operations. This creates important challenges from computational design and managerial standpoints (Cardin et al 2017). Our research focuses on developing the computational tools, digital processes, stochastic optimisation, and machine learning algorithms that will support better design and decision-making in such a deeply uncertain, and heavily data-driven environment (de Neufville et al 2019;Kuznetsova et al 2019).…”

Flexibility Management for Space Logistics Through Decision Rules

“…On the other hand, the decision rule, also called implementable policy, maps the observations of uncertainty data to the decisions. Cardin et al [18] showed that for a highly structured problem, optimization based on decision rules can provide a good approximation of optimal solution that is typically found by real option analysis techniques, while the solutions of decision rule were more practical and intuitive for decision-makers to use in practical applications. Numerical experiments on waste-to-energy systems in past literature showed that decision rules-based methods can find a very close solution to traditional uncertainty analysis methods with a significantly reduced computational time required [18].…”