Solving slot allocation problem with multiple ATFM measures by using enhanced meta-heuristic algorithm

• Published in: Computers & industrial engineering Vol. 160; p. 107602
• Main Authors: Tian, Jing, Hao, Xinchang, Huang, Jibo, Huang, Jinglei, Gen, Mitsuo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2021
• Subjects: Air traffic flow management; Meta-heuristic optimization; Priority-based rule; Slot allocation; Slot allocation; Air traffic flow management; Meta-heuristic optimization; Priority-based rule
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2021.107602

•Slot division method based on Minimal-Time-Interval to improve the flexibility.•Multi-dimension flight priority represents flight sequence for resource allocation.•ATFM measures with bi-level mitigate the disruption of new-coming measures.•An enhanced meta-heuristic algorithm based on EDA improves optimality. In Air Traffic Flow Management (ATFM), one key issue is to determine the Calculated Take Off Time (CTOT) for each flight. Due to the limited slot time resources, one single flight’s slot decision could be influenced by plenty of other flights. Few existing approaches in the previous literatures consider the interaction among various flights, so that the possible solution achieved will perform low stability and performance, especially under the condition of multiple measures. Aiming to solve the resource and flights coupling problems, in this paper, we present one heuristic method based on multi-dimension flight priority, with novel proposed methods of slot division and flight ranking. Meanwhile, in order to improve the optimality and flexibility of solutions, Estimation of Distribution Algorithm (EDA) is adopted. As one meta-heuristic algorithm, EDA is used to optimize the assignment sequence based on probability model, by which to provide an enhanced approach to solve slot allocation optimization problems. The numerical experiments have been conducted, the comparison results show that our approach has high calculation performance compared with conventional meta-heuristic algorithm-based approach, and better stability and applicability for re-allocation under the dynamic situation.