Hidden Markov Models for Pattern Learning and Recognition in a Data-Driven Model for Airline Disruption Management

Ogunsina, Kolawole; Papamichalis, Marios; Bilionis, Ilias; DeLaurentis, Daniel A.

doi:10.2514/6.2019-3508

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…In this simulation analysis, we use an airline data set to evaluate the models that consider long-term temporal trends for the airline scheduling process. From a raw data set, which contains 1.3 million direct flights (data points) defined by forty different features over a one-year period provided by a major U.S. airline (Ogunsina et al, 2019), this section describes the methods used to abstract and encode different data features in the data set in order to achieve high fidelity probabilistic graphical models. Since Bayesian network algorithms operate better with continuous data, all categorical values in the data set must be encoded into usable and valid continuous data before being used in a Bayesian inference.…”

Section: Application: Aviation Industry Schedulingmentioning

confidence: 99%

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

Papamichalis¹,

Ray²,

Bilionis³

et al. 2021

Preprint

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Probabilistic machine learning models are often insufficient to help with decisions on interventions because those models find correlations -not causal relationships. If observational data is only available and experimentations are infeasible, the correct approach to study the impact of an intervention is to invoke Pearl's causality framework. Even that framework assumes that the underlying causal graph is known, which is seldom the case in practice. When the causal structure is not known, one may use out-of-the-box algorithms to find causal dependencies from observational data. However, there exists no method that also accounts for the decision-maker's prior knowledge when developing the causal structure either. The objective of this paper is to develop rational approaches for making decisions from observational data in the presence of causal graph uncertainty and prior knowledge from the decision-maker. We use ensemble methods like Bayesian Model Averaging (BMA) to infer set of causal graphs that can represent the data generation process. We provide decisions by computing the expected value and risk of potential interventions explicitly. We demonstrate our approach by applying them in different example contexts.

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Section: Application: Aviation Industry Schedulingmentioning

confidence: 99%

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

Papamichalis¹,

Ray²,

Bilionis³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Since many algorithms for learning probabilistic graphical models perform best with continuous data (Getoor & Taskar, 2007;Ogunsina et al, 2021Ogunsina et al, , 2019, (Liskov, 1988;Reid Turner et al, 1999). (Seger, 2018).…”

Section: Feature Engineeringmentioning

confidence: 99%

Relational Dynamic Bayesian Network Modeling for Uncertainty Quantification and Propagation in Airline Disruption Management

Ogunsina¹,

Papamichalis²,

DeLaurentis³

2021

Preprint

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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 This article represents sections of a chapter from the corresponding author's completed doctoral dissertation.

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Hidden Markov Models for Pattern Learning and Recognition in a Data-Driven Model for Airline Disruption Management

Cited by 2 publications

References 7 publications

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known

Relational Dynamic Bayesian Network Modeling for Uncertainty Quantification and Propagation in Airline Disruption Management

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