Proximal point algorithms for convex multi-criteria optimization with applications to supply chain risk management

We study a class of convex multi-criteria optimization problems with convex objective functions under linear constraints. We use a non-scalarization method-namely, two implementable proximal point algorithms-to obtain the Pareto optimum under multi-criteria optimization. We show that the algorithms are globally convergent. We apply the algorithms to a supply chain risk management problem under multi-criteria considerations.