Metaheuristic algorithms for the simultaneous slot allocation problem

In this paper, we formalize the simultaneous slot allocation problem. It is an extension of the problem currently tackled for allocating airport slots: it deals with all airports simultaneously and it enforces the respect of airspace sector capacities. By solving this novel problem, the system may overcome some major inefficiencies that characterize the current slot allocation process. We tackle the simultaneous slot allocation problem with two algorithms based on metaheuristics, namely Iterated Local Search and Variable Neighborhood Search, and with an integer linear programming model: for each of these three algorithms, we allow a fixed computation time, and we take the best solution found during that time as the final solution. We compare these algorithms on randomly generated instances, and we show that, when small instances are to be tackled, metaheuristics are competitive with the exact model. When medium or large instances are to be tackled, the exact model suffers some major issues in terms of memory and computation time requirements. Metaheuristics, instead, can deal with very large instances, achieving very high quality results.