Models for single-sector stochastic air traffic flow management under reduced airspace capacity. Issue 1 (1st January 2016)

• Record Type: Journal Article
• Authors: Chang, Yu-Heng; Solak, Senay; Clarke, John-Paul B; Johnson, Ellis L
• Is Part Of: Journal of the Operational Research Society. Volume 67:Issue 1(2016)
• Page Start: 54
• Page End: 67
• Publication Date: 2016-01-01
• Subjects: air traffic flow management -- sector capacity -- Lagrangian relaxation -- rolling horizon; Operations research -- Periodicals; 658.4034
• DOI: 10.1057/jors.2015.53
• Languages: English
• ISSNs: 0160-5682

Abstract

Abstract: Air traffic efficiency is heavily influenced by unanticipated factors that result in capacity reduction. Of these factors, weather is the most significant cause of delays in airport and airspace operations. Considering weather-related uncertainty, air traffic flow management involves controlling air traffic through allocation of available airspace capacity to flights. The corresponding decision process results in a stochastic dynamic problem where aircraft on the ground and in the air are controlled based on the evolution of weather uncertainty. We focus on the single-sector version of the problem that is applicable to a majority of cases where a volume of airspace has reduced capacity due to convective weather. We model the decision process through stochastic integer programming formulations and computationally analyse it for tractability. We then demonstrate through actual flight schedule data that a simplistic but practically implementable approximation procedure is a generally effective solution approach for these models.