Robust Optimization with Scenarios Using Belief Functions

In this paper a class of optimization problems with uncertain objective function coefficients is considered. The uncertainty is specified by providing a scenario set containing a finite number of parameter realizations, called scenarios. Additional knowledge about scenarios is modeled by specifying a mass function, which defines a belief function in scenario set. The generalized Hurwicz criterion is then used to compute a solution. Various computational properties of the resulting optimization problem are presented.

Automated summary

This paper examines a class of optimization problems with uncertain objective function coefficients, using a scenario set and mass function to specify additional knowledge, and utilizing the generalized Hurwicz criterion to calculate solutions. Various computational properties of the resulting optimization problem are presented.