Data-Driven Approaches for Large-Scale Optimization

用于大规模优化的数据驱动方法

• 批准号: RGPIN-2017-03999
• 负责人: Elhedhli, Samir
• 金额: $ 2.04万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=636802
• 关键词: Data; Driven; Approaches; Large; Scale

Faced with massive amounts of data that has been collected for over a decade, companies from a variety of industries, such as transportation and telecommunication, are looking to transform this data to information in order to create competitive advantage. Most importantly, they are hoping to use it to assess, optimize, and validate their operations, processes, and business models. With data availability and the improvements in computational power, there is an opportunity to build optimization models that make direct use of the data.

面对十多年来收集的大量数据，来自运输和电信等各行各业的公司都希望将这些数据转换为信息，以创造竞争优势。最重要的是，他们希望使用它来评估、优化和验证他们的运营、流程和业务模型。随着数据可用性和计算能力的提高，有机会建立直接使用数据的优化模型。