Comptes Rendus
no. 10
A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle
Mohamed Nejlaoui1  ; Ajmi Houidi2  ; Zouhaier Affi1  ; Lotfi Romdhane3
1 National School of Engineers, University of Monastir, Tunisia
2 Higher Institute of Applied Sciences and Technology, Sousse, Tunisia
3 College of Engineering, The American University of Sharjah, Sharjah, United Arab Emirates
Comptes Rendus. Mécanique, Volume 345 (2017) no. 10, pp. 712-723.

This paper deals with the robust safety design optimization of a rail vehicle system moving in short radius curved tracks. A combined multi-objective imperialist competitive algorithm and Monte Carlo method is developed and used for the robust multi-objective optimization of the rail vehicle system. This robust optimization of rail vehicle safety considers simultaneously the derailment angle and its standard deviation where the design parameters uncertainties are considered. The obtained results showed that the robust design reduces significantly the sensitivity of the rail vehicle safety to the design parameters uncertainties compared to the determinist one and to the literature results.

Reçu le :
Accepté le :
Publié le :
DOI : 10.1016/j.crme.2017.05.014
Mots clés : Optimization, Rail vehicle, Curved tracks, Safety, Robust design, Uncertainty
Mohamed Nejlaoui 1 ; Ajmi Houidi 2 ; Zouhaier Affi 1 ; Lotfi Romdhane 3

1 National School of Engineers, University of Monastir, Tunisia
2 Higher Institute of Applied Sciences and Technology, Sousse, Tunisia
3 College of Engineering, The American University of Sharjah, Sharjah, United Arab Emirates 
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Mohamed Nejlaoui; Ajmi Houidi; Zouhaier Affi; Lotfi Romdhane. A hybrid multi-objective imperialist competitive algorithm and Monte Carlo method for robust safety design of a rail vehicle. Comptes Rendus. Mécanique, Volume 345 (2017) no. 10, pp. 712-723. doi : 10.1016/j.crme.2017.05.014. https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.1016/j.crme.2017.05.014/

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