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We present a multi‐fidelity black‐box optimization approach for integrated design and control (IDC) of constrained nonlinear systems in the presence of uncertainty. The IDC framework is becoming increasingly important for the systematic design of next‐generation (flexible) manufacturing and energy systems. However, identifying optimal solutions to realistic IDC problems is intractable when (i) the dynamics occur on much shorter timescales than the system lifetime, (ii) the uncertainties are described by continuous random variables with high variance, and (iii) operational decisions involve a mixture of discrete and continuous variables. Instead of aggressively simplifying the problem to improve tractability, we develop a simulation‐based optimization procedure using high‐quality decision rules that map information that can be measured online to optimal control actions. In particular, we rely on the Bayesian optimization (BO) framework that has been shown to perform very well on noisy and expensive‐to‐evaluate objective functions. We also discuss how BO can be extended to take advantage of computationally cheaper low‐fidelity approximations to the high‐fidelity IDC cost function. Three major low‐fidelity approximation strategies are described in this work, which are related to the simplification of the system simulator, decision rule solution method, and time grid. Lastly, we demonstrate the advantages of multi‐fidelity BO on the design of a solar‐powered building heating/cooling system (with battery and grid support) under uncertain weather and demand conditions with hourly variation over a year‐long planning horizon.

Objective: Assessment of the cost-effectiveness of strategies to scale up cesarean sections (CS) Design: Cost-effectiveness analysis to evaluate three different strategies to scale up CS Setting: Rural and urban areas of India with varying rates of CS and access to comprehensive emergency obstetric care (CEmOC) Population: Women of reproductive age in India Methods: Three strategies with different access to CEmOC and CS rates were evaluated: (A) India’s national average (50.2% access, 17.2% CS rate), (B) rural areas (47.2% access, 12.8% CS rate) and (C) urban areas (55.7% access, 28.2% CS rate). We performed a first-order Monte Carlo simulation using a 1-year cycle time and 35-year time horizon. All inputs were derived from literature. A societal perspective was utilized with a willingness-to-pay threshold of $1,940. Main outcome measures: Costs and quality-adjusted life years were used to calculate the incremental cost-effectiveness ratio (ICER). Maternal and neonatal outcomes were calculated. Results: Strategy C with the highest access to CEmOC despite the highest CS rate was cost-effective, with an ICER of 354.90. Two-way sensitivity analysis demonstrated this was driven by increased access to CEmOC. The highest CS rate strategy had the highest number of previa, accreta and ICU admissions. The strategy with the lowest access to CEmOC had the highest number of fistulae, uterine rupture, and stillbirths. Conclusions: Morbidity and mortality result from lack of access to CEmOC and overuse of CS. While interventions are needed to address both, increasing access to surgical obstetric care drives cost-effectiveness and is paramount to optimize outcomes.