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

A. Azadeh (), M. Khakestani, S. Motevali Haghighi, A. Arjmand and Z. Jiryaei Sharahi
Additional contact information
A. Azadeh: University of Tehran
M. Khakestani: Tarbiat Modares University
S. Motevali Haghighi: University of Tehran
A. Arjmand: Al Zahra University
Z. Jiryaei Sharahi: University of Yazd

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 2, No 50, 1176 pages

Abstract: 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.

Keywords: Decision-making styles; Adaptive network based fuzzy inference system (ANFIS); Human reliability; Optimization; Petrochemical plant (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-017-0582-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-017-0582-z

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-017-0582-z

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().