Constraint-Guided Local Search for Single Mixed-Operation Runway

Aircraft sequencing problem (ASP) is to schedule the operation times of departing and arriving aircraft such that their deviation from the desired operation times are minimised. There are two types of hard constraint which make this problem very challenging: time window constraint for the operation time of each aircraft, and minimum separation time between each pair of aircraft. ASP is known to be NP-Hard. Although some progress has been made in recent years in solving ASP, existing techniques still rely on generic algorithms that usually lack problem specific knowledge. This leads to either finding low quality solutions or scrambling with large-sized problems. In this work, we propose a constraint-guided local search algorithm that advances ASP search by injecting the specific knowledge of the problem into its different phases. In the intensification phase, we propose a greedy approach that gives more priorities to aircraft that are more problematic and create more delays. In the diversification phase, we employ a bounded-diversification technique that controls the new position of each selected aircraft and does not allow them to move very far away from their current positions. Computational results show that the proposed algorithm outperforms the existing state-of-the-art methods with considerable margin.