A new algorithm for linearly constrained c-convex vector optimization with a supply chain network risk application

• Published in: European journal of operational research Vol. 247; no. 2; pp. 359 - 365
• Main Authors: Qu, Shaojian, Goh, Mark, Ji, Ying, De Souza, Robert
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.12.2015; Elsevier Sequoia S.A
• Subjects: Algorithms; C-convex; Mathematical functions; Multiple objective programming; Network flow problem; Optimization; Optimization algorithms; Pareto optimum; Proximal point algorithm; Risk management; Studies; Supply chain management; Supply chain network risk management; Supply chains; Vector space; Supply chain network risk management; Pareto optimum; Proximal point algorithm; Multiple objective programming; C-convex
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2015.06.016

•Vector optimization is studied.•A proximal point algorithm is proposed for vector optimization.•The global and local convergence results for the new algorithm are presented.•The efficiency of the new algorithm is shown by an application to a supply chain network risk management problem. We study a class of vector optimization problems with a C-convex objective function under linear constraints. We extend the proximal point algorithm used in scalar optimization to vector optimization. We analyze both the global and local convergence results for the new algorithm. We then apply the proximal point algorithm to a supply chain network risk management problem under bi-criteria considerations.