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: 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=650059
• 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. ******Incorporating massive data, however, introduces substantial optimization challenges that cannot be handled by current methods. Heuristics can tackle these problems, but come with no guarantee. Data mining techniques can be used to highlight specific aspects of the data that can be used to construct good solutions. Data Analytics offer a variety of quick and efficient techniques that can serve this purpose. Some of them are easily accessible through software libraries such as R or Python, and perform well in practice.*******The current proposal exploits the efficiency of these techniques to devise solutions that come with a quality guarantee on the solution. For example, optimizing the design of a logistics network will involve decisions on the location of distribution centres and the assignment of demand to them. By mining demand data over time, it could be possible to identify demand clusters that would most probably be the optimal location for distribution centres. By doing so, we have solved the first part of the logistics network design problem. The second part, the assignment of individual demand to the distribution centres, can be done based on a heuristic or by solving an easier optimization problem. At the end, a feasible network design is achieved. What remains is whether it is the best. This step involves the use of advanced optimization techniques such as inverse optimization to devise a lower bound against which the solution is compared. If it is revealed that it is far from being optimal, an other iteration is performed. This same approach would apply to the design of a telecommunication network, an emergency response system, or a call center based on call data.******The proposal is expected to initiate a new research direction in the solution of large and very large-scale optimization problems and to open the door towards solving some of the very challenging practical problems. Data, if mined properly, would reveal these characteristics.*Our experience with this approach for the solution of mixed-case palletization problems in the warehousing industry is very promising. The problem is essentially that of optimally forming pallets based on customer demand. We mine data to reveal boxes with common characteristics that could be grouped together to form layers. Layers are then stacked to form pallets.**

面对十多年来收集的大量数据，来自运输和电信等各个行业的公司都在寻求将这些数据转化为信息，以创造竞争优势。最重要的是，他们希望使用它来评估、优化和验证他们的操作、流程和业务模型。随着数据的可用性和计算能力的提高，有机会构建直接使用数据的优化模型。******然而，整合大量数据带来了当前方法无法处理的大量优化挑战。启发式方法可以解决这些问题，但不能保证。数据挖掘技术可用于突出数据的特定方面，这些方面可用于构建良好的解决方案。数据分析提供了各种快速有效的技术来实现这一目的。其中一些可以通过诸如R或Python之类的软件库轻松访问，并且在实践中表现良好。*******当前的建议利用这些技术的效率来设计解决方案，并在解决方案上提供质量保证。例如，优化物流网络的设计将涉及对配送中心的位置和分配需求的决策。通过长期挖掘需求数据，有可能确定最有可能成为配送中心最佳位置的需求集群。这样，我们就解决了第一部分的物流网络设计问题。第二部分，个人需求分配到配送中心，可以基于启发式或通过解决一个更容易的优化问题来完成。最后给出了一个可行的网络设计方案。剩下的问题是它是否是最好的。这一步涉及到使用先进的优化技术，如逆优化，以设计一个下界的解决方案进行比较。如果发现它远非最优，则执行另一次迭代。同样的方法也适用于电信网络、应急响应系统或基于呼叫数据的呼叫中心的设计。******该提案有望在解决大型和超大规模优化问题方面开创一个新的研究方向，并为解决一些非常具有挑战性的实际问题打开大门。数据，如果挖掘得当，将揭示这些特征。*我们在解决仓储行业混合码垛问题方面的经验非常有前途。问题本质上是基于客户需求的最佳成型托盘。我们挖掘数据以揭示具有共同特征的盒子，这些盒子可以组合在一起形成层。层然后堆叠形成托盘