Second Degree Chance Constraints with Lognormal Random Variables - An Application to Fisher's Discriminant Function for Separation of Populations

In this paper, we have discussed a transformation procedure of the second-degree chance constraints to the deterministic constraints for mathemat ical programming problems having general second degree chance constraints with lognormal random variables. We have used geometric inequality fo r this transformat ion. The transformed deterministic problem having non-linear constraints and linear or non-linear objective function can be solved using non-linear programming algorith m. Also we have applied this model to Fisher's discriminant function for separation of populations. A numerical simulat ion have been considered along with a graphical representation of the reduced solution region.