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

Shao-Jian Qu (), Mark Goh, Robert Souza and Tie-Nan Wang
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Shao-Jian Qu: Harbin Institute of Technology
Mark Goh: National University of Singapore
Robert Souza: National University of Singapore
Tie-Nan Wang: Harbin Institute of Technology

Journal of Optimization Theory and Applications, 2014, vol. 163, issue 3, No 14, 949-956

Abstract: Abstract 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.

Keywords: Multi-criteria optimization; Pareto optimum; Proximate point algorithms; Supply chain risk management; 90C29; 90C90 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10957-014-0540-8

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