ABSTRACT
Although conventional optimization methods have been around for a long time, it is still hard to connect theory with practice, which can be seen in the fact that they are not being applied to many real-world problems. Two primary reasons for real-world optimization problems may fail to lead to practical solutions. Firstly, most real-world problems are multiobjective in nature, and secondly, there is no prior knowledge of the input data. Both issues are extensively examined in multiobjective and robust optimization. Nevertheless, only a little research has been carried out on combining these two aspects, and there is still scope for further advancement. Most real-life optimization problems may have uncertain parameter values because of prediction and estimation errors or not having enough information while making decisions. As a result, solving such uncertain optimization problems is crucial for decision-makers. Soyster first presented robust linear programs with uncertain coefficients in 1973.
