In multi-parametric programming, an optimization problem is solved for a range and as a
function of multiple parameters. In this review, we discuss the main developments of multi …

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

In this paper, we describe POP, a MATLAB toolbox for parametric optimization. It features (a)
efficient implementations of multiparametric programming problem solvers for …

We present a framework for the application of design and control optimization via multi‐
parametric programming through four case studies. We develop design dependent multi …

In hybrid model predictive control (MPC), a mixed-integer quadratic program (MIQP) is
solved at each sampling time to compute the optimal control action. Although these …

Chemical process optimization and control are affected by (1) plant-model mismatch,(2)
process disturbances, and (3) constraints for safe operation. Reinforcement learning by …

This work describes a new surrogate-assisted constraint-handling technique (CHT) for
parametric multi-objective evolutionary algorithms, called Bayesian CHT. Parametric …

In this article, motivated by the need for efficient closed-loop implementation of the control
objectives set within the integrated planning, scheduling and control (iPSC) problem we …

In this paper, we present a strategy for the exact solution of multiparametric quadratically
constrained quadratic programs (mpQCQPs). Specifically, we focus on multiparametric …

Major application areas of the process systems engineering, such as hybrid control,
scheduling and synthesis can be formulated as mixed integer linear programming (MILP) …