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Socio-Technical Modeling and Simulation of Airline Operations Control
von Zimmer NicoExtended abstract of the dissertation „Socio-Technical Modeling and Simulation of Airline Operations Control“
Abstract The goal of an Airline Operations Control Center (AOCC) is to maintain the airline schedule during each day-of- operation in order to ensure that passengers and cargo are safely and efficiently transported to their destination at the scheduled time. AOCC personnel are regularly challenged through anthropogenic or natural disruptions resulting in Irregular Operations (IRROPS). IRROPS cause canceled flights, aircraft delays, and diversions. Most optimization research concerning the AOCC to date has focused on disruption recovery strategies through mathematical Operations Research (OR) methods. The social aspect and especially the direct influence of the human in the loop is disregarded, even though still critical to operational effectiveness. This research effort aims at revealing the potential human factor influence on operational workflow effectiveness during IRROPS. A Socio-Technical System (STS) approach is discovered as an applicable framework for researching the complex environment of an AOCC, as it puts equal weight on both systems. State-of-the-art psychological and organizational research shows that the social subsystem and especially the individual’s personality as long-term predictor influences occupational behavior and job effectiveness. Computer science research suggests the Belief-Desire- Intentions (BDI)-agent concept as an abstraction for the human practical reasoning which is considered to model the individual’s intentions. State-of-the-art cognitive science research suggests to include the Five-Factor Model (FFM) as an extension to the BDI-agent concept, in order to support human psychological founded behavior. The AOCC environment is modeled through the STS approach consisting of four key elements: people and structure (social subsystem) as well as tasks and technology (technical subsystem). The fundamentals of the AOCC’s day of operation and disruption management workflows are researched and described. Current research works on modeling and simulation of the AOCC is classified according to the STS framework. Personality as human factor influence to the social subsystem is identified as research gap. In an exploratory study, Operations Control Centers (OCCs) within Air Traffic, Rail Traffic, and Energy Supply are compared to the AOCC with respect to the social subsystem. Findings show that the OCC purpose and objective can be different, but the people and structure elements of the social subsystem are similar. An Agent-Oriented Modeling (AOM) methodology is used to analyze the AOCC and represent a typical environment based on goal, role, organization, and domain model for the day of operation. A platform-independent model of personality-based behavior for AOCC personnel is designed in which the FFM represents people’s personality within the AOCC. The model is focused on Conscientiousness (C) and Agreeableness (A) out of the FFM, as both have shown significant differences between a norm population and an AOCC population in research findings. The platform-specific implementation is based on the BDI-agent architecture. A simulation model is composed in order to simulate a typical airline day of operation scenario incorporating AOCC workflow, technical systems, and people. A reference use case represents an irregular scenario with different possible scenario solutions. Experiments are defined that compare subjects of a norm population with an AOCC population and the latter with two more notional AOCC populations in order to examine the relationship between subject and solution. Monte-Carlo simulations reveal that the type of subject used has a significant effect on what disruption solution has been chosen to solve the scenario. Other experiments show that the hypothesis of higher C leading to improved operational effectiveness is supported, whereas very low to low A leads to ambivalent operational results. Further validation and experiments with regard to personality traits – and especially on A – are suggested. Nevertheless, management and human resource departments are supported in their AOCC personality choice and may consider the fact that the social subsystem, and especially the human factor of it, has a significant contribution to the overall STS performance. The results indicate that knowledge about the people and their behavior could help to design greater system acceptance. Management of change which encompasses the social subsystem is advisable to airline management when technical systems are introduced into the AOCC environment; therefore an applicable future research topic.
Abstract The goal of an Airline Operations Control Center (AOCC) is to maintain the airline schedule during each day-of- operation in order to ensure that passengers and cargo are safely and efficiently transported to their destination at the scheduled time. AOCC personnel are regularly challenged through anthropogenic or natural disruptions resulting in Irregular Operations (IRROPS). IRROPS cause canceled flights, aircraft delays, and diversions. Most optimization research concerning the AOCC to date has focused on disruption recovery strategies through mathematical Operations Research (OR) methods. The social aspect and especially the direct influence of the human in the loop is disregarded, even though still critical to operational effectiveness. This research effort aims at revealing the potential human factor influence on operational workflow effectiveness during IRROPS. A Socio-Technical System (STS) approach is discovered as an applicable framework for researching the complex environment of an AOCC, as it puts equal weight on both systems. State-of-the-art psychological and organizational research shows that the social subsystem and especially the individual’s personality as long-term predictor influences occupational behavior and job effectiveness. Computer science research suggests the Belief-Desire- Intentions (BDI)-agent concept as an abstraction for the human practical reasoning which is considered to model the individual’s intentions. State-of-the-art cognitive science research suggests to include the Five-Factor Model (FFM) as an extension to the BDI-agent concept, in order to support human psychological founded behavior. The AOCC environment is modeled through the STS approach consisting of four key elements: people and structure (social subsystem) as well as tasks and technology (technical subsystem). The fundamentals of the AOCC’s day of operation and disruption management workflows are researched and described. Current research works on modeling and simulation of the AOCC is classified according to the STS framework. Personality as human factor influence to the social subsystem is identified as research gap. In an exploratory study, Operations Control Centers (OCCs) within Air Traffic, Rail Traffic, and Energy Supply are compared to the AOCC with respect to the social subsystem. Findings show that the OCC purpose and objective can be different, but the people and structure elements of the social subsystem are similar. An Agent-Oriented Modeling (AOM) methodology is used to analyze the AOCC and represent a typical environment based on goal, role, organization, and domain model for the day of operation. A platform-independent model of personality-based behavior for AOCC personnel is designed in which the FFM represents people’s personality within the AOCC. The model is focused on Conscientiousness (C) and Agreeableness (A) out of the FFM, as both have shown significant differences between a norm population and an AOCC population in research findings. The platform-specific implementation is based on the BDI-agent architecture. A simulation model is composed in order to simulate a typical airline day of operation scenario incorporating AOCC workflow, technical systems, and people. A reference use case represents an irregular scenario with different possible scenario solutions. Experiments are defined that compare subjects of a norm population with an AOCC population and the latter with two more notional AOCC populations in order to examine the relationship between subject and solution. Monte-Carlo simulations reveal that the type of subject used has a significant effect on what disruption solution has been chosen to solve the scenario. Other experiments show that the hypothesis of higher C leading to improved operational effectiveness is supported, whereas very low to low A leads to ambivalent operational results. Further validation and experiments with regard to personality traits – and especially on A – are suggested. Nevertheless, management and human resource departments are supported in their AOCC personality choice and may consider the fact that the social subsystem, and especially the human factor of it, has a significant contribution to the overall STS performance. The results indicate that knowledge about the people and their behavior could help to design greater system acceptance. Management of change which encompasses the social subsystem is advisable to airline management when technical systems are introduced into the AOCC environment; therefore an applicable future research topic.