Improved prediction of mental workload versus HSE and ergonomics factors by an adaptive intelligent algorithm

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This study proposes an adaptive intelligent algorithm for forecasting and improving mental workload among operators with respect to HSEE in large gas Treatment Company. The algorithm is composed of reliability, verification, validation and forecasting mechanisms through robust mathematical and statistical methods, Artificial Neural Network (ANN) and Adaptive Network Based Fuzzy Inference System Algorithm (ANFIS). To achieve the objectives of this study, standard questionnaires with respect to HSEE are completed by operators. The average results for each category of HSEE are used as inputs and mental workload is used as output in the algorithm. The efficiency of ANN is examined against ANFIS by use of Mean Absolute Percentage Error (MAPE). The results concluded that ANN provides better solutions than ANFIS. Therefore, ANN is used to forecast and rank operators performance with respect to HSEE and mental workload. Normal probability technique is used to identify outlier operators. Moreover, operators with inadequate mental workload with respect to HSEE are identified. This would help managers to foresee if operators are satisfied with their mental workload in the context of HSEE. Finally, sensitivity analysis of the intelligent algorithm is shown by error analysis in contrast with conventional regression approaches. This is the first study that introduces an adaptive algorithm for efficient forecasting of mental workload with respect to HSEE program in complex systems.