We consider an unconstrained multi-criteria optimization problem with finite sum objective functions. The proposed algorithm belongs to a non-monotone trust-region framework where additional sampling approach is used to govern the sample size and the acceptance of a candidate point. Depending on the problem, the method can result in a mini-batch or an increasing sample size … Read more
This paper introduces a novel algorithm for Mixed-Integer Nonlinear Programming (MINLP) problems with multilinear interpolations of look-up tables. These problems arise when objectives or constraints contain black-box functions only known at a finite set of evaluations on a predefined grid. We derive a piecewise-linear relaxation for the multilinear interpolants, which require an MINLP formulation. Supported … Read more
We developed an interpretability informed Bayesian optimization framework to optimize underwater acoustic coatings based on polyurethane elastomers with embedded metamaterial features. A data driven model was employed to analyze the relationship between acoustic performance, specifically sound absorption and the corresponding design variables. By leveraging SHapley Additive exPlanations (SHAP), a machine learning interpretability tool, we identified … Read more
Here we propose a novel, fully automated framework for accelerated design of underwater acoustic coatings, targeting the reduction of acoustic signature of naval platforms under operational conditions, by coupling Bayesian Optimization (BO) with a 2-step Finite Element Model (FEM). The developed FEMs evaluate the acoustic performance of polyurethane (PU) coatings with embedded metamaterial features under … Read more
The widespread existence of various uncertainties makes the inherently complex refinery production scheduling problem even more challenging. To address this issue, this paper proposes a viable systematic data-driven multistage scheduling optimization framework and develops a corresponding structured modeling methodology. Under this paradigm, unit-level advanced control and plant-level intelligent scheduling are coordinated to jointly deal with … Read more
Real world problems often require complex modeling and computation efforts to be effectively addressed. Relying solely on data-driven approaches without integrating physics-based models can result in limited predictive capabilities. Even advanced techniques such as deep learning may be impractical for decision-makers due to the lack of data and challenges in justifying and explaining results. In … Read more
We revisit the problem of ensuring strong test-set performance via cross-validation. Motivated by the generalization theory literature, we propose a nested k-fold cross- validation scheme that selects hyperparameters by minimizing a weighted sum of the usual cross-validation metric and an empirical model-stability measure. The weight on the stability term is itself chosen via a nested … Read more
Security-constrained unit commitment with alternating current optimal power flow (SCUC-ACOPF) is a central problem in power grid operations that optimizes commitment and dispatch of generators under a physically accurate power transmission model while encouraging robustness against component failures. SCUC-ACOPF requires solving large-scale problems that involve multiple time periods and networks with thousands of buses within … Read more
Maintenance strategies are pivotal in ensuring the reliability and performance of critical components within industrial machines and systems. However, accurately determining the optimal replacement time for such components under stress and deterioration remains a complex task due to inherent uncertainties and variability in operating conditions. In this paper, we propose a comprehensive approach based on … Read more
Gas transportation and storage has become one of the most relevant and important optimization problems in energy systems. This problem inherently includes highly nonlinear and nonconvex aspects due to gas physics, and discrete aspects due to the control decisions of active network elements. Obtaining even locally optimal solutions for this problem presents significant mathematical and … Read more