Proximal Point Algorithms for Convex Multi-criteria Optimization with Applications to Supply Chain Risk Management

摘要

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.