Bilevel optimization is a field of mathematical programming in which some variables are
constrained to be the solution of another optimization problem. As a consequence, bilevel …

The inevitable presence of uncertain parameters in critical applications of process
optimization can lead to undesirable or infeasible solutions. For this reason, optimization …

Rising populations put huge stresses on natural resources. Extraction and depletion of raw
materials and waste created throughout the supply chain of products have enormous …

Abstract Machine learning models are promising as surrogates in optimization when
replacing difficult to solve equations or black-box type models. This work demonstrates the …

The coordination of interconnected elements across the different layers of the supply chain
is essential for all industrial processes and the key to optimal decision-making. Yet, the …

Fitting a machine learning model often requires presetting parameter values
(hyperparameters) that control how an algorithm learns from the data. Selecting an optimal …

This paper presents a hybrid Model Predictive Control (hMPC) approach to improve Electric
Vehicle (EV) stability when cornering or in high-risk driving conditions. By using …

Optimization problems involving two decision makers at two different decision levels are
referred to as bi-level programming problems. In this work, we present novel algorithms for …

Abstract The Data-driven Optimization of bi-level Mixed-Integer NOnlinear problems
(DOMINO) framework is presented for addressing the optimization of bi-level mixed-integer …

Adjustable robust optimization (ARO) involves recourse decisions (ie reactive actions after
the realization of the uncertainty,'wait-and-see') as functions of the uncertainty, typically …