Data-Driven Approaches for Large-Scale Optimization

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。** 然而，对海量数据进行分类会带来大量的优化挑战，这些挑战无法通过当前的方法来处理。启发式可以解决这些问题，但没有保证。数据挖掘技术可用于突出可用于构建良好解决方案的数据的特定方面。 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R或Python等软件库轻松访问，并且在实践中表现良好。目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决定配送中心的位置和向它们分配需求。通过挖掘一段时间内的需求数据，有可能确定最有可能成为配送中心最佳地点的需求集群。通过这样做，我们已经解决了物流网络设计问题的第一部分。第二部分，分配的个人需求的配送中心，可以根据启发式或通过解决一个更容易的优化问题。最后给出了一个可行的网络设计方案。剩下的就是它是否是最好的。这一步骤涉及使用高级优化技术，如逆优化，以设计一个下限，与解决方案进行比较。如果发现它远不是最优的，则执行另一次迭代。同样的方法也适用于电信网络、紧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。数据如果得到适当挖掘，将揭示这些特征。我们的经验，这种方法的解决方案的混合情况下，在仓储行业的仓储问题是非常有前途的。问题本质上是基于客户需求最佳地形成托盘。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。然后将层堆叠以形成托盘。**

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.**