This paper reviews recent advances in the field of optimization under uncertainty via a
modern data lens, highlights key research challenges and promise of data-driven …

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 …

Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize
fossil-based power and industrial sectors and is a bridging technology for a sustainable …

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 …

This paper proposes a novel data-driven robust optimization framework that leverages the
power of machine learning and big data analytics for decision-making under uncertainty. By …

In this work, we provide an overview of our previously published works on incorporating
demand uncertainty in midterm planning of multisite supply chains. A stochastic …

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 …

Distributionally robust optimization (DRO) is an emerging and effective method to address
the inexactness of probability distributions of uncertain parameters in decision-making under …

This article first reviews recent developments in process synthesis and discusses some of
the major challenges in the theory and practice in this area. Next, the article reviews key …

In this article, the design and retrofit problem of a supply chain (SC) consisting of several
production plants, warehouses and markets, and the associated distribution systems, is …