Canada Excellence Research Chair in Data Science for Real-time Decision Making

加拿大实时决策数据科学卓越研究主席

• 批准号: 10009000002-2018
• 负责人: Lodi, Andrea
• 金额: $ 101.99万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Canada Excellence Research Chairs
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=657903
• 关键词: Canada; Excellence; Research; Chair; Data

The CERC objective is on developing new methodologies to exploit "Big Data" and achieve optimized strategies for the current (and future)· era of decision-making.**That involves multi-thread computing, mobile sensors, cheap data storage and**real-time communication and these ingredients define absolutely new and exciting challenges that strongly urge for new algorithms .**Those challenges will be tackled by a tight cooperation between Mathematical Optimization and Machine Learning. The scenario is centered on data. This is because:**• Data is dynamic, i.e., not anymore to be seen as simply defining the "instance" to be solved. The methodology must be tailored to data changing in real time, and often the algorithms to be developed by Mathematical Optimization need to re-optimize over (slightly) changed conditions of traffic networks, of power grids, of supply-chain systems, etc.**• Data is available but complex, i.e., the amount of information reflects the tight integration between problems traditionally addressed separately. Then, Mathematical" Optimization together with Machine Learning must be able to extract the "right" type and quantity of information leading to the definition of the "right" problems to be solved.**• Data is big, i.e., all algorithms both on the learning and optimization sides must go large scale. This reflects the amount of data involved and the complexity of a decision-making process that looks, as never done before, to a much broader picture of the system to be managed and optimized.**It is expected that using a mixture of mixed-integer linear and nonlinear programming as well as metaheuristics and constraint programming techniques, will solve applications in logistics, supply chain, revenue management, design and operation of energy networks, etc. However, often motivated by applications, the chair research focus will be on the innovative integration of those techniques and on the development of new paradigms exploiting both Machine Learning and Mathematical Optimization for data-driven decision-making.

CERC的目标是开发新方法来探索“大数据”并为当前（和未来）的决策时代实现优化的策略。**涉及多线程计算，移动传感器，廉价的数据存储和**实时通信，这些成分定义了对新的Algoriths的强烈挑战的绝对新的和令人兴奋的挑战。该方案以数据为中心。这是因为：**•数据是动态的，即，没有被视为简单地定义要解决的“实例”。该方法必须定制为实时更改数据，并且通常需要通过数学优化来开发的算法需要重新挑选（稍微更改）流量网络条件，电力网，电网，供应链系统的条件，供应链系统等。然后，数学“与机器学习的优化必须能够提取导致要解决的“正确”问题的“正确”类型和数量。可以预期，使用混合企业线性和非线性编程以及元启发式和约束编程技术的混合，将解决在物流，供应链，收入管理，能源网络的设计，设计和操作等中的应用。但是，通常是通过应用程序激励的，椅子研究重点是对新技术的开发以及对新技术的创新整合的启发，这将是启发的， 决策。