Optimization of large-scale real-time problems in urban contexts

该研究计划围绕城市环境中出现的实时处理海量数据的现实问题展开。随着新技术的使用，实时数据采集可以更好、更快和更知情地做出决定。在这项研究中，我们使用实时信息为城市物流、交通规避和水分配做出更好的决策；这些问题对社会和行业都很重要。*关于运输，将人和货物从一个点运送到另一个点是一项复杂的任务。由于交通流量的不同，可用的各种模式和不同的成本/时间使其不可能找到“一刀切”的解决方案。最快的方式可能不是最便宜的，可能不是最环保的，对用户来说可能不是最方便的。在这里，优化在管理这些相互矛盾的目标方面扮演着重要的角色，目标是根据一组标准确定最佳解决方案。此外，诸如紧急交付之类的实时请求可能会以不希望和不可预测的方式影响解决方案。许多输入参数随时间变化，这就是所谓的时间相关问题，这可能很容易预测，也可能不容易预测。*对于更主动地处理实时数据的操作，做出实时决策是很重要的。在动态的城市交通场景中，信息不断变化，重要的是能够及时做出反应，以便在实践中解决问题。当卡车已经在执行其提货和/或交付活动，并且出现新的请求时，就会出现这种情况。必须迅速整合这些新信息，以便提供不影响公司底线、满足服务级别要求并尊重所有运营约束的及时解决方案。其中一些问题已经出现在文献中，但使用实时信息和考虑随机数据的频率要低得多。为了让工业界使用我们的解决方案，我们计划纳入这些方面，不假设未来的所有信息都是已知的，而只是部分信息，而一些信息是随着时间的推移而实现的。*饮用水是城市向其公民提供的主要服务之一。巨大的管道网络紧密相连，水流受到不同的压力和力。与在街道上行驶的车辆或在生产线上移动的货物不同，水流受到高度非线性流动条件的影响，导致压力和能量损失。利用我们从运输中获得的知识来优化饮用水分配，我们创建的算法能够提供有关质量问题(如余氯、水龄、流向等)的操作计划。从运行的角度来看，挑战是如何有效地使用泵来节约能源，并满足所有将压力保持在一定范围内的需求。

This research program is centered around real-life problems arising in urban contexts dealing with huge amounts of data in real-time. With the use of new technologies, real-time data acquisition allows for better, faster and more informed decisions to be made. In this research, we use real-time information to make better decisions for city logistics, traffic avoidance, and water distribution; these problems are important for society and for industry. ***Regarding transportation, moving people and goods from one point to another is a complicated task. The myriad of modes available and different costs/times due to traffic makes it impossible to find a “one solution fits all”. The fastest way may not be the cheapest, which may not be the most environmental-friendly, which may not be the most convenient for the user. Here, optimization plays an important role in managing these contradicting objectives with the goal of determining the best solution according to a set of criteria. Moreover, real-time requests such as an urgent delivery may affect the solution in undesirable and unpredictable ways. Many input parameters change over time, in what is known as time-dependent issues, which may or may not be easily predicted.***For more proactive operations dealing with real-time data, it is important to make real-time decisions. In dynamic urban traffic scenarios, information constantly changes, and it is important to be able to react in time allowing to solve problems in practice. This is the case when a truck is already performing its pickup and/or delivery activities, and new requests arise. These new information must be quickly incorporated in order to provide timely solutions that do not affect the bottom line of the companies, that satisfy service level requirements, and that respect all operational constraints. Some of these problems are already emerging in the literature, but the use of real-time information and considering stochastic data is much less frequent. In order to allow industry to use our solutions, we plan to incorporate these aspects, by not assuming all future information is known, but only parts of it, while some information is realized over time.***Drinking water is one of the main services that cities provide to its citizens. A huge network of pipes is strongly connected, and the flow of water is subject to different pressures and forces. Unlike vehicles traveling through streets or goods moving through a production line, water flowing is subject to highly non-linear flow conditions, causing pressure and energy loss. Using our knowledge from transportation to optimize drinking water distribution we create algorithms capable of providing operational plans respecting quality issues (such as chlorine residuals, water age, flow direction, etc). From an operational perspective, the challenge is to efficiently use pumps to save energy and to satisfy all demand keeping pressure within bounds.