时间：2022年4月2日上午8:40到9:10

报告题目：智能工业的系统优化

报告人：唐立新 院士

报告人单位：东北大学

主持人：张海涛

内容摘要：

Systems optimization is the core basic theory of decision-making in smart industry, as well as the heart and engine of data analytics. This talk will discuss some systems modeling methods and optimization solution methods we have been working on. The systems modeling methods are to quantitatively describe different practical problems with proper formulations, including set-packing model, space-time network model, and continuous-time based model. The optimization solution methods include: 1) Integer optimization to optimally solve typical combinatorial optimization problems based on mathematical programming. According to the structure features of the problems, different methods are designated including branch-and-price, Lagrangian relaxation, Benders decomposition, outer approximation, and branch-and-cut. 2) Convex optimization is the core of machine learning. It is also used to solve practical continuous optimization problem. Major methods are discussed, such as gradient descent, alternative direction, second order cone, and semidefinite. Additionally, duality theories are used to improve their efficiency. 3) Intelligent optimization to solve the large-scale optimization problems with high non-linearity, dynamics, or multi-objectives. Various intelligent optimization algorithms will be discussed, including incremental dynamic DE algorithm, individual-dependent DE algorithm, and MOEA algorithm. 4) Topology optimization is used to scientifically design material layout within a given physical space, so as to maximize the system performance while satisfying a given set of loads and boundary conditions and constraints. It is widely used in lightweight design for mechanical equipment in smart industry. Major topology optimization solution methods to handle discrete structure and continuum structure are discussed. Overall, systems optimization provides the scientific basis for decision-making and data analytics in smart industry.

报告人简介：

唐立新，现为中国工程院院士，东北大学副校长（科技规划、国际合作），东北大学控制科学与工程（自动化）国家一级重点学科负责人、控制科学与工程国家“双一流”学科建设领导小组组长，智能工业数据解析与优化教育部重点实验室主任、工业智能与系统优化国家级前沿科学中心主任和首席科学家、计算机软件国家工程研究中心工业软件首席设计师。现兼任国务院学位委员会第八届控制科学与工程学科评议组成员、教育部科技委人工智能专委会副主任、中国运筹学会副理事长兼智能工业数据解析与优化专业委员会主任。

主要研究方向为工业智能与系统优化理论方法，包括工业大数据科学、数据解析与机器学习、深度学习与进化学习、加强学习与动态优化、凸优化与稀疏优化、整数与组合最优化、计算智能优化等理论方法，智能工业全流程生产与库存计划、生产与物流批调度、生产过程操作优化与最优控制等工程优化技术，质量预报、工况监测等知识发现和图像、语音、可视等感知理解方面的数据解析技术，以及在制造、物流和能源系统中的工程应用。

在IEEE Transactions on Evolutionary Computation、IEEE Transactions on Cybernetics、IEEE Transactions on Control Systems Technology、IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Power Systems、Operations Research、Manufacturing & Service Operations Management、INFORMS Journal on Computing、IISE Transactions、Naval Research Logistics等国际重要期刊发表127篇论文。发表在国际工业与系统工程旗舰期刊IISE Transactions的论文被评为2017年度“最佳应用论文奖”（Best Applications Paper Award）。

现为6个国际工业智能与系统优化领域重要SCI期刊IISE Transactions, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, Journal of Scheduling, International Journal of Production Research, Journal of the Operational Research Society的Associate Editor，国际期刊Annals of Operations Research编委，国际期刊Asia-Pacific Journal of Operational Research区域主编(Area Editor)。受邀担任INFORMS International 2018的Cluster Chair，9th IFAC Conference on Manufacturing Modelling, Management and Control的Track Chair。