@conference { ISI:000391354500050,
	title = {Feature selection and parameter optimization of support vector regression for electric load forecasting},
	booktitle = {2016 INTERNATIONAL CONFERENCE ON ELECTRICAL AND INFORMATION TECHNOLOGIES (ICEIT)},
	year = {2016},
	note = {2nd International Conference on Electrical and Information Technologies (ICEIT), Tangier, MOROCCO, MAY 04-07, 2016},
	pages = {288-293},
	abstract = {Forecasting of future electricity demand has become a promising issue for the electric power industry. Since many factors affect electric load data, machine learning methods are useful for electric load forecasting (ELF). On the one hand, it is important to determine the irrelevant factors as a preprocessing step for ELF. On the other hand, the performance of machine learning models depends heavily on the choice of its parameters. These problems are known respectively as feature selection and model selection problems. In this paper, we use the support vector regression (SVR) model for ELF. Our contribution consists of investigating the use the particle swarm optimization for both feature selection and model selection problems. Experimental results on two widely used electric load dataset show that our proposed hybrid method for feature selection and parameter optimization of SVR can achieve better results when compared with the classical SVR model while using feature selection and without using it.},
	isbn = {978-1-4673-8469-8},
	author = {Sarhani, Malek and El Afia, Abdellatif},
	editor = {Essaaidi, M and ElHani, S}
}
@conference { ISI:000392439200025,
	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{\textquoteright}16)},
	year = {2016},
	note = {3rd IEEE International Conference on Logistics Operations Management (GOL), Fes, MOROCCO, MAY 23-25, 2016},
	publisher = {Sidi Mohammed Ben Abdellah Univ Fes, Fac Sci \& Technol; Mohammed V Univ Rabat, ENSIAS Sch; Univ Havre; IEEE},
	organization = {Sidi Mohammed Ben Abdellah Univ Fes, Fac Sci \& Technol; Mohammed V Univ Rabat, ENSIAS Sch; Univ Havre; IEEE},
	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.},
	isbn = {978-1-4673-8571-8},
	author = {Sarhani, Malek and Ezzinbi, Omar and El Afia, Abdellatif and Benadada, Youssef},
	editor = {Alaoui, AE and Benadada, Y and Boukachour, J}
}
@conference { ISI:000346582400013,
	title = {An Extension of X13-ARIMA-SEATS to Forecast Islamic Holidays Effect on Logistic Activities},
	booktitle = {PROCEEDINGS OF 2014 2ND IEEE INTERNATIONAL CONFERENCE ON LOGISTICS AND OPERATIONS MANAGEMENT (GOL 2014)},
	year = {2014},
	note = {2nd IEEE International Conference on Logistics Operations Management (GOL), Rabat, MOROCCO, JUN 05-07, 2014},
	pages = {79-84},
	publisher = {IEEE; Mohammed VI Souissi Univ, ENSIAS Sch; Univ Le Havre; Sidi Mohamed Ben Abdellah Univ, FST},
	organization = {IEEE; Mohammed VI Souissi Univ, ENSIAS Sch; Univ Le Havre; Sidi Mohamed Ben Abdellah Univ, FST},
	abstract = {To better manage and optimize logistic activities, factors that affect it must be determined: The calendar effect is one of these factors which must be analyzed. Analyzing such kind of data by using classical time series forecasting methods, such as exponential smoothing method and ARIMA model, will fail to capture such variation. This paper is released to present a review of the models which are used to forecast the calendar effect, especially moving holidays effect. We adopt the recent approach of X13-ARIMA-SEATS and extend it for being able to forecast the effect of Islamic holidays. Our extension is applied to Moroccan case studies, and aims to give recommendations concerning this effect on logistic activities.},
	isbn = {978-1-4799-4650-1},
	author = {Sarhani, Malek and El Afia, Abdellatif},
	editor = {Benadada, Y}
}
@conference { ISI:000380387700064,
	title = {Intelligent System Based Support Vector Regression For Supply Chain Demand Forecasting},
	booktitle = {2014 SECOND WORLD CONFERENCE ON COMPLEX SYSTEMS (WCCS)},
	year = {2014},
	note = {2014 Second World Conference on Complex Systems (WCCS), Agadir, MOROCCO, NOV 10-12, 2014},
	pages = {79-83},
	publisher = {Ibn Zohr Univ; Moroccan Soc of Complex Syst; IEEE Morocco; Int Acad for Syst and Cybernet Sci IASCYS},
	organization = {Ibn Zohr Univ; Moroccan Soc of Complex Syst; IEEE Morocco; Int Acad for Syst and Cybernet Sci IASCYS},
	abstract = {Supply chain management (SCM) is an emerging field that has commanded attention from different communities. On the one hand, the optimization of supply chain which is an important issue, requires a reliable prediction of future demand. On the other hand, It has been shown that intelligent systems and machine learning techniques are useful for forecasting in several applied domains. In this paper, we introduce the machine learning technique of time series forecasting Support Vector Regression (SVR) which is nowadays frequently used. Furthermore, we use the Particle Swarm Optimization (PSO) algorithm to optimize the SVR parameters. We investigate the accuracy of this approach for supply chain demand forecasting by applying it to a case study.},
	isbn = {978-1-4799-4647-1},
	author = {Sarhani, Malek and El Afia, Abdellatif}
}
@conference { ISI:000346582400008,
	title = {A metaheuristic approach for solving the airline maintenance routing with aircraft on ground problem},
	booktitle = {PROCEEDINGS OF 2014 2ND IEEE INTERNATIONAL CONFERENCE ON LOGISTICS AND OPERATIONS MANAGEMENT (GOL 2014)},
	year = {2014},
	note = {2nd IEEE International Conference on Logistics Operations Management (GOL), Rabat, MOROCCO, JUN 05-07, 2014},
	pages = {48+},
	publisher = {IEEE; Mohammed VI Souissi Univ, ENSIAS Sch; Univ Le Havre; Sidi Mohamed Ben Abdellah Univ, FST},
	organization = {IEEE; Mohammed VI Souissi Univ, ENSIAS Sch; Univ Le Havre; Sidi Mohamed Ben Abdellah Univ, FST},
	abstract = {In the airline industry, the Aircraft Maintenance Routing (AMR) problem has been one of the great successes of operations research. The AMR problem is to determine a particular route for each aircraft to undergo different levels of maintenance checks. The objective is to minimize the total maintenance costs. In this study, our aim is to present a mathematical formulation for the AMR problem which takes into account the case of Aircraft On Ground (AOG). We develop solution approaches based on Particle Swarm Optimization algorithm and Genetic algorithm for solving the problem. The results show the effectiveness of this solution in reducing computational time.},
	isbn = {978-1-4799-4650-1},
	author = {Ezzinbi, Omar and Sarhani, Malek and El Afia, Abdellatif and Benadada, Youssef},
	editor = {Benadada, Y}
}
@conference { ISI:000346582400009,
	title = {Particle swarm optimization algorithm for solving airline crew scheduling problem},
	booktitle = {PROCEEDINGS OF 2014 2ND IEEE INTERNATIONAL CONFERENCE ON LOGISTICS AND OPERATIONS MANAGEMENT (GOL 2014)},
	year = {2014},
	note = {2nd IEEE International Conference on Logistics Operations Management (GOL), Rabat, MOROCCO, JUN 05-07, 2014},
	pages = {52-56},
	publisher = {IEEE; Mohammed VI Souissi Univ, ENSIAS Sch; Univ Le Havre; Sidi Mohamed Ben Abdellah Univ, FST},
	organization = {IEEE; Mohammed VI Souissi Univ, ENSIAS Sch; Univ Le Havre; Sidi Mohamed Ben Abdellah Univ, FST},
	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.},
	isbn = {978-1-4799-4650-1},
	author = {Ezzinbi, Omar and Sarhani, Malek and El Afia, Abdellatif and Benadada, Youssef},
	editor = {Benadada, Y}
}
