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

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 cap...

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Publicado en:Scientific World Journal Vol. 2015; pp. 742541 - 742542
Autores principales: Cai, Kaiquan, Jia, Yaoguang, Zhu, Yanbo, Xiao, Mingming
Formato: Journal Article
Publicado: Wiley-Blackwell 1/1/2015
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A Novel Biobjective Risk-Based Model for Stochastic Air Traffic Network Flow Optimization Problem.
      aug:
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          Cai, Kaiquan
          Jia, Yaoguang
          Zhu, Yanbo
          Xiao, Mingming
        affil: School of Electronics and Information Engineering, Beihang University, Beijing 100191, China ; National Key Laboratory of CNS/ATM, Beijing 100191, China.
      sug:
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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