Uncertainties are widespread in the optimization of process systems, such as uncertainties
in process technologies, prices, and customer demands. In this paper, we review the basic …

Optimization under uncertainty has been an active area of research for many years.
However, its application in Process Systems Engineering has faced a number of important …

Deterministic optimization approaches have been well developed and widely used in the
process industry to accomplish off-line and on-line process optimization. The challenging …

In this paper, we present a generic mixed-integer linear multistage stochastic programming
(MSSP) model considering endogenous uncertainty in some of the parameters. To address …

This paper develops a two-stage stochastic programming approach for process planning
under uncertainty. We first extend a deterministic mixed-integer linear programming …

A large number of problems in production planning and scheduling, location, transportation,
finance, and engineering design require that decisions be made in the presence of …

The problem of selecting an optimal design/plan for process models involving stochastic
parameters is addressed in this paper. A classification of uncertainty is introduced …

The need to model uncertainty in process design and operations has long been recognized.
A frequently taken approach, the two-stage paradigm, involves partitioning the problem …

In stochastic process design and planning optimization problems, the expected value of the
objective function in face of uncertainty is typically evaluated through an n-dimensional …

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 …