The Man-machine Integration Design and Analysis (MIDAS) human performance model was augmented to improve predictions of multi-operator situation awareness (SA). In MIDAS, the environment is defined by situation elements (SE) that are processed by the modeled operator via a series of sub-models including visual attention, perception, and memory. Collectively, these sub-models represent the situation assessment process and determine which SEs are attended to and comprehended by the modeled operator. SA is computed as a ratio of the Actual SA (the number of SEs that are detected or comprehended) relative to the Optimal SA (those deemed required or desired for the operator to complete his/her task).A high-fidelity application model of a two-pilot commercial crew during the approach phase of flight was generated to demonstrate and verify the SA model. Two flight deck display configurations, hypothesized to support pilot SA at differing levels, were modeled. The results presented include the ratio of actual to optimal SA for three high-level tasks: Aviate, Separate, and Navigate. The model results verified that the SA model operates as expected and is sensitive to scenario characteristics including display configuration and pilot responsibilities.
There are not always sufficient resources or time available to ident.i@ human factors issues early enough for development of detailed technical bases using empirical experimentation with human subjects.
An analytical approach to addressing the implications of nuclear power plant shift sizing is needed as an augmentation to the classical empirical approach. The research reported in this paper was to evaluate the feasibility and validity of one potential analytical approach as a means of evaluating the consequences of crew reduction on crew performance in a nuclear power plant setting. The approach selected for analysis was task network modeling and simulation using a tool named Micro Saint. Task network modeling allows the human factors engineer to extend the information from a task analysis and generate a computer simulation of crew performance that can predict critical task times and error rates. Through modeling, the current and proposed processes can be evaluated and analyzed in order to understand, identify, and test opportunities for process improvement or reengineering. For this effort, models of a conventional nuclear power plant during four extremely demanding scenarios were developed. Task analysis and timing data were collected at the Imatran Voima Nuclear Power Plant at Loviisa, Finland. The task analyses were collected over a two week period by interviewing reactor operators, reviewing procedures, and conducting walk-throughs. We then refined the models and incorporated workload modeling constructs. At the completion of the modeling effort, the models were executed and the data collected were used to predict crew performance in varying staffing conditions.
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