Christine SOLNON, Associate Professor, LIRIS Laboratory

Smart cities are equipped with sensors which monitor many different physical conditions (such as temperature, sound, or traffic speed). These sensor networks generate huge data flows which are analyzed to obtain time-dependent predicted data such as, for example, the evolution of traffic velocity in a city road network during a typical week day. These time-dependent predicted data must be efficiently exploited to optimize resource consumption and, more generally, drive sustainable economic growth for citizens. Hence, we have designed new optimization tools to efficiently solve these time-dependent optimization problems. In this talk, we shall illustrate the interest of these tools for optimising urban delivery tours.