Robust Optimization

"A specific and relatively novel methodology for handling mathematical optimization problems with uncertain data."

In my own words:

The objective of robust optimization (RO) is to find solutions that are immune to the uncertainty of the parameters in a mathematical optimization problem. It requires that the constraints of a given problem should be satisfied for all realizations of the uncertain parameters in a so-called uncertainty set. The robust version of a mathematical optimization problem is generally referred to as the robust counterpart (RC) problem. RO is popular because of the tractability of the RC for many classes of uncertainty sets, and its applicability in wide range of topics and practice.

One of the most important references for the field is the book, Robust Optimization, written by Ben-Tal, Nemirovski and El Ghaoui (2009).  

 

Also see the following often cited RO papers:

• Robust convex optimization. 1998. Ben-Tal and Nemirovski.

• Robust optimization – methodology and applications. 2002. Ben-Tal and Nemirovski. 

• Robust discrete optimization and network flows. 2003. Bertsimas and Sim. 

• The price of robustness. 2004. Bertsimas and Sim.

• A practical guide to robust optimization. 2015. Gorissen et al. (link)

• A survey of adjustable robust optimization. 2019. Yanıkoğlu et al. (link)

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Other fields
Stochastic Programming, Logistics, IE/OR Applications, Machine Learning