Chance constrained programming approach to process optimization under uncertainty
By Pu Li, H. Arellano‐Garcia, Günter Wozny. Computers and Chemical Engineering, 2008
343 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/3730880dc22d9416248dc119ec5013fcdf31f162
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In Control and Systems Engineering
In Statistics and Probability
Citations in the corpus
- Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, methodology): “Due to its practical relevance, chance constrained optimization has been applied in numerous applications, including model predictive control [76, 77], process design and operations [78-80], refinery blend planning [81], biopharmaceutical manufacturing [82], and supply chain optimization [83-86].”
- A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty (2011, background): “, and, thereafter, issues of CCP were widely investigated [222–236].”
- Chance Constrained Programming for Optimal Power Flow Under Uncertainty (2011, methodology): “Then these values will be sent to an NLP solver where the sequential quadratic programming (SQP) method is implemented [8], to search for an updated control vector, and this will be sent to the simulation layer to run the next iterate.”