Uncertainty is a very important concern in production scheduling since it can cause
infeasibilities and production disturbances. Thus scheduling under uncertainty has received …

The process systems engineering (PSE) as well as the operations research and
management science (ORMS) literature has hitherto focused on disparate processes and …

In this paper, model predictive control (MPC) based optimization problems with a quadratic
performance criterion and linear constraints are formulated as multi-parametric quadratic …

In this paper, we present a brief overview of enterprise-wide optimization and challenges in
multiscale temporal modeling and integration of different models for the levels of planning …

In this work we propose algorithms for the solution of multiparametric quadratic
programming (mp-QP) problems and multiparametric mixed-integer quadratic programming …

In this paper, a stochastic programming framework is presented to address process
synthesis problems under uncertainty. The framework is based on a two-state stochastic …

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

A two-stage, stochastic programming approach is proposed for incorporating demand
uncertainty in multisite midterm supply-chain planning problems. In this bilevel decision …

In this paper, a parametric programming approach is proposed for the analysis of linear
process engineering problems under uncertainty. A novel branch and bound algorithm is …

An Annotated Bibliography for Post-Solution Analysis in Mixed Integer Programming and
Combinatorial Optimization | SpringerLink Skip to main content Advertisement SpringerLink …