Sample average approximation of multiobjective stochastic optimization problems with application in insurance

Abstract

The article describes a technique for solving multiobjective stochastic optimization problems. As a generalized model of a stochastic system to be optimized a vector (input-random output) system is used. Ran- dom outputs are converted into a vector of deterministic performance and risk indicators. The problem is to find those inputs that correspond to a Pareto-optimal values of output indicators. The problem is approximated by a sequence of deterministic multi-criteria optimization problems, where, for example, the objective vector function is a sample average approximation of the original one and the feasible set is a discrete sample approximation of the feasible inputs. Approximate optimal solutions are defined as weakly Pareto-efficient ones within some vector tolerance. Convergence analysis includes establishing convergence of the general approx- imation scheme and establishing conditions of convergence with probability one under proper regulation of sampling parameters. The proposed technique is illustrated on a computer system for supporting multi-criteria optimization of insurance business.