@conference {Bouzbita2018, title = {Parameter adaptation for ant colony system algorithm using hidden markov model for tsp problems}, booktitle = {ACM International Conference Proceeding Series}, year = {2018}, doi = {10.1145/3230905.3230962}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053485206\&doi=10.1145\%2f3230905.3230962\&partnerID=40\&md5=69e09fa2debc5dc07106895d9ce7948b}, author = {Bouzbita, S. and El Afia, A. and Faizi, R.} } @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 {Khaldi2017, title = {Prediction of supplier performance: A novel DEA-ANFIS based approach}, booktitle = {ACM International Conference Proceeding Series}, volume = {Part F129474}, year = {2017}, note = {cited By 0}, abstract = {The focus of this paper is on investigating the feasibility of using ANFIS combined with DEA for supplier{\textquoteright}s post-evaluation. The proposed framework aims at modeling performance measurement, and forecasting of a selected hospital{\textquoteright}s drug suppliers. Even though it is broadly employed as a benchmarking tool to evaluate DMUs efficiency, DEA can hardly be used to predict the performance of unseen DMUs. For this reason, ANFIS model has been integrated to DEA due to its nonlinear mapping, strong generalization capabilities and pattern prediction functionalities. DEA based BCC model is used to evaluate the efficiency scores of a set of suppliers, then ANFIS intervenes to learn DEA patterns and to forecast the performance of new suppliers. The results of this research highlight the prediction power of the proposed model in a new scope. They present it as an efficient benchmarking tool and a promising decision support system applied at the operational level. {\textcopyright} 2017 Association for Computing Machinery.}, doi = {10.1145/3090354.3090416}, url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028459962\&doi=10.1145\%2f3090354.3090416\&partnerID=40\&md5=da01d9720d77f4831205623526bfc9d9}, author = {Khaldi, R. and Chiheb, R. and El Afia, A. and Akaaboune, A. and Faizi, R.} } @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.} }