Optimization under uncertainty: state-of-the-art and opportunities

By Nikolaos V. Sahinidis. Computers and Chemical Engineering, 2004

1,248 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b1d477e292cd2cd15eebf3dfdf879698099f2de9

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In Control and Systems Engineering

In Management Science and Operations Research

In Ocean Engineering

Citations in the corpus

Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “Motivated by the practical concern, optimization under uncertainty has attracted tremendous attention from both academia and industry [4, 9].”
Retrospective on optimization (2004, background): “Sahinidis and Grossmann (1991) have shown that if problem (P1) has zero integrality gap, the GBD algorithm converges in one iteration once the optimal ( x∗, y∗) is found.”
A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty (2011, background): “Research that considers uncertainty can be categorized according to the four primary approaches (Sahinidis, 2004): (1) Stochastic programming approach, (2) fuzzy programming approach, (3) stochastic dynamic programming approach, and (4) robust optimization approach.”
Resource Scheduling Under Uncertainty in a Smart Grid With Renewables and Plug-in Vehicles (2012, neutral): “For optimization with uncertainty [19], a set of valid scenarios is considered in the UC with plug-in vehicles for the uncertainties of wind and solar energy, load and vehicles.”
A survey on dynamic and stochastic vehicle routing problems (2016, background): “A classiﬁcation according to the stochastic parameters is proposed in Gendreau, Laporte, and Sguin (1996) and various optimisation problems with uncertainty are summarised in Sahinidis (2004).”
Simulation optimization: a review of algorithms and applications (2014, background): “ stochastic programming diﬀers in that it makes heavy use of the model structure itself [27]. Optimization under uncertainty techniques that make heavy use of mathematical programming are reviewed in [175]. Derivative-Free Optimization. Both Simulation Optimization and Derivative-Free Optimization (”
Simultaneous design, scheduling, and optimal control of a methyl-methacrylate continuous polymerization reactor (2008, neutral): “um ss;iyi;1; 8m;k = Ns ( 18 ) um 1;1;k = u m in;k; 8m;k”
Modeling and solving a large-scale generation expansion planning problem under uncertainty (2011, background): “State-of-the-art optimization methods under uncertainty are reviewed in [58], and include stochastic programming, robust optimization, probabilistic (chance-constrained) programming, fuzzy programming, and stochastic dynamic programming.”