The Need: Optimization problems are affected by uncertainties, either due to inaccuracy in measurements, lack of timely information on values of parameters (which incurs estimation errors), and limitation on executing accurately a computed solution (causing implementation errors). Due to these uncertainties a solution may divert from the expected optimality, and even worse; can lead to infeasibility. Consequently, there is a great need to come up with a method that can address the above difficulties.
The challenge is to introduce a methodology which on one hand will not impose on the user of the optimization the need to provide information on the uncertainties, which he cannot deliver, and on the other hand will not impose on the optimizer the need to solve intractable optimization problems, in particular dynamic (multi‐stage) ones. Robust Optimization (RO) is an attempt to meet these challenges. The talks will explain and demonstrate how this is achieved.
The success: RO created a new branch of optimization theory; this is evidence by huge number of publications addressing topics in RO. What is even more impressive is the large number of diverse applications of RO (Google Scholar lists over 46,800,000 items when one clicks "application of robust optimization"!)