Relational dynamic Bayesian network modeling for uncertainty quantification and propagation in airline disruption management. (June 2022)

• Record Type: Journal Article
• Authors: Ogunsina, Kolawole; Papamichalis, Marios; DeLaurentis, Daniel
• Is Part Of: Engineering applications of artificial intelligence. Volume 112(2022)
• Publication Date: 2022-06
• Subjects: Airline disruption management -- Probabilistic graphical models -- Hidden Markov models -- Intelligent systems -- Explainable artificial intelligence -- Expert systems; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104846
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Disruption management during the airline scheduling process can be compartmentalized into proactive and reactive processes depending upon the time of schedule execution. The state of the art for decision-making in airline disruption management involves a heuristic human-centric approach that does not categorically study uncertainty in proactive and reactive processes for managing airline schedule disruptions. Hence, this paper introduces an uncertainty transfer function model (UTFM) framework that characterizes uncertainty for proactive airline disruption management before schedule execution, reactive airline disruption management during schedule execution, and proactive airline disruption management after schedule execution to enable the construction of quantitative tools that can allow an intelligent agent to rationalize complex interactions and procedures for robust airline disruption management. Specifically, we use historical scheduling and operations data from a major U.S. airline to facilitate the development and assessment of the UTFM, defined by hidden Markov models (a special class of probabilistic graphical models) that can efficiently perform pattern learning and inference on portions of large data sets. We employ the UTFM to assess two independent and separately disrupted flight legs from the airline route network. Assessment of a flight leg from Dallas to Houston, disrupted by air traffic control hold for bad weather at Dallas, revealed that proactive disruption management for turnaround in Dallas before schedule execution is impractical because of zero transition probability between turnaround and taxi-out. Assessment of another flight leg from Chicago to Boston, disrupted by air traffic control hold for bad weather at Boston, showed that proactive disruption management before schedule execution is possible because of non-zero state transition probabilities at all phases of flight operation.