@conference {ElAfia2017,
	title = {Data-driven based aircraft maintenance routing by markov decision process model},
	booktitle = {ACM International Conference Proceeding Series},
	volume = {Part F129474},
	year = {2017},
	note = {cited By 0},
	abstract = {Aircraft maintenance routing is of basic significance to the safe and efficient operations of an airline. However, the timely efficiency of the airline flight schedule is susceptible to various factors during the daily operations. Air traffic often undergoes some random disruptions that expose maintenance routing to random flight delays, which have to be considered to ensure safe and operational flight schedule. The idea of data-driven methods was the focal point of much studies during a previous couple of years. Constrained Markov Decision process model was selected in this paper to remedy this problem and design the maintenance needs of an aircraft taking past data information into account. Maintenance actions are so modeled with stochastic state transitions. This can offer the opportunity to solve the maintenance routing problem deliberating and handling flight disturbances. Through computational tests on real data of a Moroccan airline company, we investigate the efficiency of this solution approach on history data sets. {\textcopyright} 2017 Association for Computing Machinery.},
	doi = {10.1145/3090354.3090430},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028455379\&doi=10.1145\%2f3090354.3090430\&partnerID=40\&md5=b414d040a3e32989b0121144dbd1f7f3},
	author = {El Afia, A. and Aoun, O.}
}
@conference {Bouzbita2017344,
	title = {Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model},
	booktitle = {Proceedings of 2016 International Conference on Cloud Computing Technologies and Applications, CloudTech 2016},
	year = {2017},
	note = {cited By 1},
	pages = {344-349},
	abstract = {The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality. {\textcopyright} 2016 IEEE.},
	doi = {10.1109/CloudTech.2016.7847719},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013861130\&doi=10.1109\%2fCloudTech.2016.7847719\&partnerID=40\&md5=26cafb2d23ee70bf2f3552d7fec22e8a},
	author = {Bouzbita, S. and El Afia, A. and Faizi, R. and Zbakh, M.}
}
