An optimal data-splitting algorithm for aircraft sequencing on a single runway

• Published in: Annals of operations research Vol. 309; no. 2; pp. 587 - 610
• Main Authors: Prakash, Rakesh, Desai, Jitamitra, Piplani, Rajesh
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-02-2022; Springer; Springer Nature B.V
• Subjects: Air traffic control; Aircraft; Airports; Algorithms; Business and Management; Combinatorics; Constraints; Dynamic programming; Linear programming; Mathematical optimization; Mixed integer; Operations research; Operations Research/Decision Theory; Optimization; Real time; Runways; S.I.: Data-Driven OR in Transportation and Logistics; Safety; Splitting; Technology application; Theory of Computation; Windows (intervals); Singapore; Aircraft sequencing; Scheduling; Constrained position shifting; 0–1 mixed-integer programming
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-021-04351-2

During peak-hour busy airports have the challenge of turning aircraft around as quickly as possible, which includes sequencing their landings and take-offs with maximum efficiency, without sacrificing safety. This problem, termed aircraft sequencing problem (ASP) has traditionally been hard to solve optimally in real-time, even for flights over a one-hour planning window. In this article, we present a novel data-splitting algorithm to solve the ASP on a single runway with the objective to minimize the total delay in the system both under segregated and mixed mode of operation. The problem is formulated as a 0–1 mixed integer program, taking into account several realistic constraints, including safety separation standards, wide time-windows, and constrained position shifting. Following divide-and-conquer paradigm, the algorithm divides the given set of flights into several disjoint subsets, each of which is optimized using 0–1 MIP while ensuring the optimality of the entire set. One hour peak-traffic instances of this problem, which is NP-hard in general, are computationally difficult to solve with direct application of the commercial solver, as well as existing state-of-the-art dynamic programming method. Using our data-splitting algorithm, various randomly generated instances of the problem can be solved optimally in near real-time, with time savings of over 90%.