Abstract. We consider the Airspace Sectorization Problem (ASP) in which airspace has to be partitioned into a given number of sectors, each of which being assigned to a team of air traffic controllers. The objective is to minimize the coordination workload between adjacent sectors while balancing the total workload of controllers. Many specific constraints, including both geometrical and aircraft related constraints are taken into account. The problem is solved in a constraint programming framework. Experimental results show that our approach can be used on real life problems.

In this paper we consider the Dynamic Airspace Sectorization Problem (DASP) where airspace is partitioned into a number of sectors, each sector being assigned to a team of air trafEc controllers. The objective of DASP is to balance the controllers' workload among the sectors and to simultaneously minimize the coordination workload between adjacent sectors.This problem is closely related to the Graph Partitioning problems. However, some specific constraints have to be taken into account (e.g., aircraft can not enter twice the same sector; aircraft have to stay in each crossed sector at least a given amount of time, etc.) and they make it difficult to use the most popular Graph Partitioning techniques of the literature. To solve the DASP, we introduce a constraint-programming formulation that can compute optimal solution for "small" instances of problem. A heuristic based on the notion of gain of Kernighdin algorithm for Graph Partitioning is also introduced for finding a reasonable good initial solution for large size instances in a small amount of time. We also show how the constraintprogramming formulation is used to re-optimize locally this initial solution.graphs show that our approach is promising.