Single-objective Optimization using Chance-constrained Model: A Comparison Result Between Linear and Normal Uncertainty Distributions

Uncertain optimization problem is an important tool in the field of optimization and are widely used in realworld applications.In this paper, we consider an uncertain single-objective optimization problem with uncertainty in the coefficients of both objective function and constraints.We formulate the proposed problem and show that the corresponding chance-constrained optimization model can be converted into a deterministic model by considering linear and normal uncertainty distributions.This paper mainly aims to provide the solution techniques for an uncertain singleobjective optimization problem and show a comparison between the optimial objective values in relation to uncertainty levels, under linear and normal uncertainty distributions.Finally, two numerical examples are presented to illustrate the usefulness of the proposed method.