@conference {ElAfia20181,
	title = {Performance prediction using support vector machine for the configuration of optimization algorithms},
	booktitle = {Proceedings of 2017 International Conference of Cloud Computing Technologies and Applications, CloudTech 2017},
	volume = {2018-January},
	year = {2018},
	pages = {1-7},
	doi = {10.1109/CloudTech.2017.8284699},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046624408\&doi=10.1109\%2fCloudTech.2017.8284699\&partnerID=40\&md5=13c42fe4df8b128f5a87988605c2485f},
	author = {El Afia, A. and Sarhani, M.}
}
@conference {ElAfia2017,
	title = {Particle swarm optimization for model selection of aircraft maintenance predictive models},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {Nowadays, predictive models -especially the ones based on machine learning- are widely used to solve many big data problems. One of the main challenges within predictive models is to choose the best model for each problem. In particular, model selection and feature selection are two important issues in machine learning models as they help to achieve the best results. This paper focuses on the restriction of these two problems to σ-SVR (support vector regression) and more specifically the optimization of both problems using the particle swarm optimization algorithm. Our approach is investigated in the estimation of remaining useful life (RUL) of aircrafts which a ects their maintenance planning and which is an interesting issue in predictive maintenances. That is, the experiment consists of predicting RUL of aircraft engines using an σ-SVR optimized by PSO. Experimental results show the efficiency of the proposed approach. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090402},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028474954\&doi=10.1145\%2f3090354.3090402\&partnerID=40\&md5=2d4328ae0f65ce92a35e5516b96bd0b7},
	author = {El Afia, A. and Sarhani, M.}
}
@conference {Sarhani2016,
	title = {Particle swarm optimization with a mutation operator for solving the preventive aircraft maintenance routing problem},
	booktitle = {Proceedings of the 3rd IEEE International Conference on Logistics Operations Management, GOL 2016},
	year = {2016},
	note = {cited By 0},
	abstract = {Aircraft Maintenance Routing (AMR) is one of the major optimization problems in the airline industry. In this study, we present a mathematical formulation for the daily AMR problem which aims to minimize the risk of both scheduled and non-scheduled maintenance costs. Exact methods may fail to deal with such problems. Our contribution is then to examine the use of an improved particle swarm optimization (PSO) algorithm by a uniform mutation operator for solving this probabilistic problem. Computational results show that our hybrid approach gives competitive results comparing to the native binary PSO. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/GOL.2016.7731683},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85001976721\&doi=10.1109\%2fGOL.2016.7731683\&partnerID=40\&md5=9bef08dc37eff9c6ed7b1c2c6e2e41b6},
	author = {Sarhani, M. and Ezzinbi, O. and Afia, A.E. and Benadada, Y.}
}
@conference {Ezzinbi201452,
	title = {Particle swarm optimization algorithm for solving airline crew scheduling problem},
	booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
	year = {2014},
	note = {cited By 1},
	pages = {52-56},
	abstract = {In air transport, the cost related to crew members presents one of the most important cost supported by airline companies. The objective of the crew scheduling problem is to determine a minimum-cost set of pairings so that every flight leg is assigned a qualified crew and every pairing satisfies the set of applicable work rules. In this paper, we propose a solution for the crew scheduling problem with Particle Swarm Optimization (PSO) algorithm, this solution approach is compared with the Genetic Algorithm (GA) for both crew pairing and crew assignment problems which are the two part of crew scheduling problem. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/GOL.2014.6887447},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908628135\&doi=10.1109\%2fGOL.2014.6887447\&partnerID=40\&md5=61f25771c33cea8f4845104ba352a303},
	author = {Ezzinbi, O. and Sarhani, M. and El Afia, A. and Benadada, Y.}
}
