This paper gives a review on existing scheduling methodologies developed for process
industries. Above all, the aim of the paper is to focus on the industrial aspects of scheduling …

This manuscript reviews recent advances in deterministic global optimization for Mixed-
Integer Nonlinear Programming (MINLP), as well as Constrained Derivative-Free …

Scientists aim to discover meaningful formulae that accurately describe experimental data.
Mathematical models of natural phenomena can be manually created from domain …

Many optimal decision problems in scientific, engineering, and public sector applications
involve both discrete decisions and nonlinear system dynamics that affect the quality of the …

This paper is concerned with the optimal influenza vaccine distribution in a heterogeneous
population consisting of multiple subgroups. We employ a compartmental model for …

This book is intended primarily for those not yet familiar with methods for computing with
intervals of real numbers and what can be done with these methods. Using a pair [a, b] of …

In recent years, optimization theory has been greatly impacted by the advent of sum of
squares (SOS) optimization. The reliance of this technique on large-scale semidefinite …

This book is intended primarily for those not yet familiar with methods for computing with
intervals of real numbers and what can be done with these methods. Using a pair [a, b] of …

Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

To decarbonize electricity grids, CO2 capture and renewable wind/solar are two promising
pathways. However, the intermittency of these variable renewable sources and the high …