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

点击翻译按钮获取中文摘要

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.