The e-Mobility case study aims at solving global problems, involving large ensembles of different vehicles. Such large problems tend to be complex to solve and often a globally optimal solution may be impossible to find. For this reason specific strategies are needed to solve them. In particular, we are interested in the parking optimization problem, consisting in finding the best parking lot for each vehicle of an ensemble. The best parking lot is chosen by considering: the distance from the current location of the vehicle to the parking lot, the distance from the parking lot to the appointment location and the cost of the parking lot.
We present two approaches to solving the parking optimization problem, joint work between Ugo Montanari, Giacoma Valentina Monreale and Matteo Sammartino at the University of Pisa, and Nicklas Hoch, at Volkswagen AG, Corporate Research Group in Wolfsburg. These approaches are based on the coordination of declarative and procedural knowledge. Declarative knowledge consists in a global description of the problem and its structure, in terms of its subproblems. General algorithms can be applied at this level, with high computational cost. Procedural knowledge defines which strategies should be applied to subproblems, and how their solutions can be combined into a possibly suboptimal, but acceptable global solution. Strategies are often more efficient than general algorithms, because they rely on specific knowledge about problems, which could also allow for the application of heuristics in order to improve the computational cost.