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Vol. 5 No. 2 (2018): JITA - APEIRON

Božo Vukoje, Vjekoslav Bobar

Solving the Chief Executive Officer Selection Problem Using the Fuzzy Decision Support System

Original scientific paper

DOI: https://doi.org/10.7251/JIT1502097V

Abstract

Chief Executive Officer (CEO) selection as a subset of personnel selection asks for different characteristic compared to a selection of other personnel. The reason for this is the polymorphic nature of the CEO role. The complexity and importance of the selection problem, call for analytical methods rather than decisions based on intuition. The multi-criteria nature and the presence of both qualitative and quantitative factors make the entire selection more complex. As such, the CEO selection is a multi-criteria decision making problem decision making problem, affected by several qualitative and quantitative, often conflicting criteria which are usually uncertain. This paper proposes a CEO selection approach based on the fuzzy decision support system developed by using JAVA technology and extent analysis method. This system is applied in a real-life case study to evaluate the most suitable person for a CEO position in information and communication (ICT) company dealing with the rating of both qualitative and quantitative criteria, and testing appropriate consistency to ensure quality of selection.

Keywords: CEO selection, fuzzy numbers, extent analysis method, decision support system.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.

Vol. 26 No. 2 (2023): JITA - APEIRON

Igor Shubinsky, Alexey Ozerov

Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures

Original scientific paper

Abstract

The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers’ expenditures.

Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.