Robust and Data-Driven Optimization: Modern Decision-Making Under Uncertainty

By Dimitris J. Bertsimas, Aurélie C. Thiele. 2006

325 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/0644a2c3ef2f6a0225c616ae9f707e5b203d4702

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chao Ning
2. Fengqi You
3. Jingru Luo
4. Kris K. Hauser

In Management Science and Operations Research

1. Adam N. Elmachtoub
2. Chao Ning
3. Fengqi You
4. Paul Grigas
5. Ryan McNellis

In Ocean Engineering

1. Chao Ning
2. Fengqi You

Citations in the corpus

• Robust trajectory optimization under frictional contact with iterative learning (2015, background): “The robust optimization models acceleration errors as range estimation (similar to [2]) and optimizes a nonlinear problem to generate a trajectory that is robust up to a given confidence value.”
• Smart "Predict, then Optimize" (2017, background): “nsfromdatainclude operationalstatistics(LiyanageandShanthikumar(2005),Chuetal.(2008)),sampleaverage approximation(Kleywegtetal.(2002),Schützetal.(2009),Bertsimasetal.(2018b)),and robust optimization (Bertsimas and Thiele (2006), Bertsimas et al. (2018a), Wang et al. (2016)).Therehasalsobeensomerecen”
• Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming (2019, background): “learning over the past decade [18], sparks a flurry of interest in data-driven optimization [19-26].”