代表性成果：坚持特色发展、学术研究与解决实际问题相结合的原则，该中心对多种近现代优化方法及其应用展开了系统研究。团队在多阶段随机优化、分布式鲁棒优化的稳定性分析与高性能算法设计等领域展开了系统研究，成果发表在SIAM J. Optim., JOTA, EJOR, JOGO等期刊，是国内唯一连续多年受邀参加国际随机规划大会的团队，团队带头人陈志平教授也因此被聘为《OR Spectrum》中国大陆唯一编委；在JBF, JEDC, IME, QF等国际顶尖金融学杂志连续发表论文，在国内外具有较大影响；由于在运用运筹学方法解决复杂金融决策问题方面的贡献突出，应邀在Springer出版的“Optimal Financial Decision Making under Uncertainty”专著中撰写综述。团队在图因子理论研究方面给出了沃尔夫奖得主Lovasz教授的(g,f)-因子结构定理的简化证明（使其证明从24页改进到5页），被国际著名图论专家Brian Alspach称为“漂亮的工作”；关于图谱刻画的工作被著名代数图论学者Brouwer和Haemers写进其专著“Spectra of Graphs”； 解决了世界数学家大会一小时报告人、美国、欧洲科学院院士Rodl教授所提的超图匹配猜想；解决了著名的Tutte类型条件刻画问题，被国际图论专家Kano教授评价为“I now understand that you are one of the top three researchers in the world of Graph Factors“；这些成果发表于SIAM J. Discrete Math.。团队提出了多目标进化算法研究领域两大主流算法之一的MOEA/D算法，成果发表于IEEE Trans. EVC，获得2010年该期刊的年度最佳论文奖，当前谷歌学术引用次数达4500次；研究了多目标稀疏优化、自适应协方差进化策略、以及变维度优化问题，成果发表在IEEE Trans. NNLS, IEEE Trans. CYB, IEEE Trans. EVC等期刊。上述成果已经初步应用于大型企业运筹管理、金融风险管理与投资分析、工程优化设计等领域的复杂决策问题。
Representative achievements: Following the principle of combining characteristic development, academic research and practical problem solution, the Center has conducted systematic researches on a variety of modern optimization methods and the applications thereof. The team carries out systematic researches in such fields as stability analysis and high-performance algorithm design of multi-stage stochastic optimization and distributionally robust optimization , the results of which have been published in SIAM J. Optim., JOTA, EJOR, JOGO and like journals. This is the only team from China that has been invited for consecutive years to attend the International Conference on Stochastic Programming, and the team leader Professor Chen Zhiping is also the only editorial board member of "OR Spectrum" in Chinese Mainland. The team also consecutively published papers in top international financial journals such as JBF, JEDC, IME and QF. With their outstanding contribution to applying operations research methods to solving complex financial decision-making problems, the team was invited to write a review in the monograph "Optimal Financial Decision Making under Uncertainty" published by Springer. In terms of the research on the graph factor theory, the team simplified the proof of the (g,f)-factor structure theorem of Professor László Lovász, the Wolf Prize winner (the proof is simplified from 24 pages to 5 pages), and Brian Alspach, a globally renowned graph theory expert, called it "good job"; the team’s work on the characterization of graph spectrum was included by Andries E. Brouwer and Willem H. Haemers, two famous algebraic graph theorists, into their monograph "Spectra of Graphs"; the team solved the hypergraph matching conjecture proposed by Professor Vojtěch Rödl, the one-hour speaker of the World Congress of Mathematicians and a member of the American Academy of Science and the Academy of Europe, and solved the well-known Tutte type condition characterization problem, which is evaluated as "I now understand that you are one of the top three researchers in the world of Graph Factors" by Professor Noriaki Kano, an international graph theory expert. And these results were published in SIAM J. Discrete Math. The team has proposed the MOEA/D algorithm, one of the two mainstream algorithms in the field of multi-objective evolutionary algorithms, and the result was published in IEEE Trans. EVC, received the Best Paper Award of the journal in 2010, and has been cited for 4,500 on Google Scholar; and researched multi-objective sparse optimization, adaptive covariance evolution strategy and variable-dimensional optimization problems, the results of which were published in IEEE Trans. NNLS, IEEE Trans. CYB, IEEE Trans. EVC and other journals. The above achievements have been initially applied to complex decision-making problems in the fields such as large enterprise operation management, financial risk management and investment analysis, and optimization of engineering design.
Research directions: The Center follows the principle of "Conducting academic research for solving real-world OR problems and applying academic research results to better solving OR problems" in the work, keep up with academic trends, and focuses on key areas such as quantitative finance and social networks. With the support of the major projects of the National Natural Science Foundation of China and the industrial projects of large state-owned enterprises, the Center is committed to developing new optimization technologies, so as to provide technical support for solving complex decision-making problems arising in various fields. The current research directions include:
Theories and algorithms of dynamic stochastic optimization and distributionally robust optimization;
Optimization techniques in quantitative finance and their application;
Intelligent optimization techniques for multi-objective optimization problems with complex features;
Research on algorithms for NP-hard combination optimization problems based on reinforcement learning;
Research on combinatorial optimization problems in wireless sensor networks and social networks.