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The Scientific World Journal
Volume 2015, Article ID 742541, 10 pages
http://dx.doi.org/10.1155/2015/742541
Research Article

A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem

1School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
2National Key Laboratory of CNS/ATM, Beijing 100191, China
3Aviation Data Communication Corporation, Beijing 100191, China

Received 5 December 2014; Accepted 9 January 2015

Academic Editor: Kemao Peng

Copyright © 2015 Kaiquan Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Network-wide air traffic flow management (ATFM) is an effective way to alleviate demand-capacity imbalances globally and thereafter reduce airspace congestion and flight delays. The conventional ATFM models assume the capacities of airports or airspace sectors are all predetermined. However, the capacity uncertainties due to the dynamics of convective weather may make the deterministic ATFM measures impractical. This paper investigates the stochastic air traffic network flow optimization (SATNFO) problem, which is formulated as a weighted biobjective 0-1 integer programming model. In order to evaluate the effect of capacity uncertainties on ATFM, the operational risk is modeled via probabilistic risk assessment and introduced as an extra objective in SATNFO problem. Computation experiments using real-world air traffic network data associated with simulated weather data show that presented model has far less constraints compared to stochastic model with nonanticipative constraints, which means our proposed model reduces the computation complexity.