Health, Safety, Environment and Ergonomic Improvement in Energy Sector Using an Integrated Fuzzy Cognitive Map–Bayesian Network Model

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Pourreza, P
Saberi, M
Azadeh, A
Chang, Elizabeth
Hussain, Omar
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2018
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Abstract

Health, safety, environment and ergonomics (HSEE) are important factors for any organization. In fact, organizations always have to assess their compliance in these factors to the required benchmarks and take proactive actions to improve them if required. In this paper, we propose a fuzzy cognitive map–Bayesian network (BN) model in order to assist organizations in undertaking this process. The fuzzy cognitive map (FCM) method is used for constructing graphical models of BN to ascertain the relationships between the inputs and the impact which they will have on the quantified HSEE. Using the notion of Fuzzy logic assists us to work with humans and their linguistic inputs in the process of experts’ opinion solicitation. The noisy-OR method and the EM are used to ascertain the conditional probability between the inputs and quantifying the HSEE value. Using this, we find out that the most influential input factor on HSEE quantification which can then be managed for improving an organization’s compliance to HSEE. Finding the same influential input factor in both BN models which are based on the noisy-OR method and EM demonstrate how FCM is useful in constructing a reliable BN model. Leveraging the power of Bayesian network in modelling HSEE and augmenting it with FCM is the main contribution of this research work which opens the new line of research in the area of HSE management.

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International Journal of Fuzzy Systems

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20

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4

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Artificial intelligence

Applied mathematics

Science & Technology

Technology

Automation & Control Systems

Computer Science, Artificial Intelligence

Computer Science, Information Systems

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Pourreza, P; Saberi, M; Azadeh, A; Chang, E; Hussain, O, Health, Safety, Environment and Ergonomic Improvement in Energy Sector Using an Integrated Fuzzy Cognitive Map–Bayesian Network Model, International Journal of Fuzzy Systems, 2018, 20 (4), pp. 1346-1356

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