Reducing the blocking effect in the airport slot allocation problem with seasonal flexibility

Capacity limitations, combined with increased air-traffic, continue to drive the need for better resource management at airports. At congested airports, the allocation of resources for flights to take-off and land is governed through the use of slots. Slots are allocated twice a year according to the Worldwide Airport Slot Guidelines (WASG). A principle of WASG is to allocate slots to a request at the same time and day of the week over a given period, to maintain regularity in a schedule. Adhering to this principle can result in blocking, where requests are displaced to an alternative time period due to lack of available capacity. This paper addresses blocking by allowing a request to be allocated to slightly different times on different operating days. We present a novel integer linear programming (ILP) model and develop a two-stage framework, incorporating the Late Acceptance Hill Climbing metaheuristic, to increase flexibility across a complete scheduling season to mitigate the effect of blocking on three real-world congested airport instances. Results show that exact methods are not tractable for larger problems, as they are unable to find a solution in reasonable computational time, resulting in the need for heuristic methods to solve such problems in practice. Schedules created using the proposed framework are directly compared to an existing state-of-the-art model which aims to mitigate the effect of blocking through seasonal segmentation. The proposed approach is demonstrated to be able to reduce the effect of blocking, reduce the total number of displaced requests, and the maximum displacement within slot schedules when compared with non-flexible schedules. •Late Acceptance Hill Climbing is applied to the slot allocation problem.•Exact algorithms perform poorly when considering seasonal flexibility.•Improvement in schedule displacement and fewer displaced requests.•Significant run time improvement when compared to existing state-of-the-art.