A unique intelligent algorithm for optimization of human reliability and decision styles: a large petrochemical plant

Abstract

The importance of decision-making failures in complex systems such as petrochemical plants has been well recognized. Decision making failures are mostly resulted from human error. In this paper, efficiency of human operator is assessed based on combination of decision style (DS) and human reliability. At first, a standard questionnaire is designed to collect required data. The reliability of the collected data is investigated by Cronbach’s alpha (about 0.75). Also, the efficiency is ranked according to the impact of decision styles on human reliability factors. Operators of control rooms in a petrochemical plant are respond to a human reliability-decision styles (HR-DS) hybrid questionnaire. The indicators related to decision styles and human reliability are used as inputs and outputs, respectively in a unique adaptive network-based fuzzy inference system (ANFIS) algorithm. Decisive, hierarchical, flexible and integrated are the standard categorization of human decision styles. The optimum structure of ANFIS is selected based on minimum mean absolute percentage (MAPE). The best MAPE is achieved about 26%. Relative analysis shows that the efficient decision style achieved from ANFIS is consistent with plant’s dominant style. The results of this study help managers to enhance the system performance by using the best operator for critical positions. This study presents the first neuro-fuzzy algorithm for improvement of decision making and human reliability in a large petrochemical plant.