@mastersthesis {7312,
	title = {Contribution {\`a} la surveillance des syst{\`e}mes de production par les Syst{\`e}mes Multi-Agents Collectifs},
	year = {2020},
	author = {Ouahiba Chouhal}
}
@proceedings {7283,
	title = {Reverse Logistics with Disassembly, Assembly, Repair and Substitution},
	journal = {IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)},
	year = {2020},
	address = {Singapore, Singapore},
	abstract = {
	A reverse logistics planning problem is modeled and analyzed. The model considers returns of a particular electronic device from customers. Some of the collected products are remanufactured or refurbished. Others are disassembled for their key parts which can be considered as good as new. New products are assembled either using new parts or extracted ones. There are two types dynamic demands: demands for remanufactured/refurbished products and demands for new products. Demand of remanufactured/refurbished products can be satisfied using new products in case of shortage. This is a one way downward substitution. The objective is to minimize total costs while satisfying all demands. This problem is formulated as a MILP. The numerical results show that: i) it is hard for a solver to find optimal solutions for the problem in reasonable computational times for several instances with relatively small time horizons and ii) substitution is justified for a certain range of cost and demand parameters.

},
	author = {Boutarfa, Youcef and Senoussi Ahmed and Brahimi, N}
}
@proceedings {7277,
	title = {Applications of Bayesian networks in Chemical and Process Industries: A review},
	journal = {29th European Safety and Reliability Conference, August 26, 2019},
	year = {2020},
	abstract = {Despite technological advancements, chemical and process industries are still prone to accidents due to their complexity and hazardous installations. These accidents lead to significant losses that represent economic losses and most importantly human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, it consists of different methods such as Fault tree, Bow-tie, and Bayesian network. The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. Bayesian networks approaches have shown a significant increase in their application as shown by in the publication in this field. This paper summarizes the result of a literature review performed on Bayesian network approaches adopted to conduct risk assessments, safety and risk analyses. Different application domains are analysed (i.e. accident modelling, maintenance area, fault diagnosis) in chemical and process industries from the year 2006 to 2018. Furthermore, the advantages of different types of Bayesian networks are presented.},
	url = {https://livrepository.liverpool.ac.uk/3054267/1/Overview_of_BN_in_chemical_and_process_industries.pdf},
	author = {Zerrouki, Hamza and Estrada-Lugo, Hector Diego and Hacene Smadi and Patelli, Edoardo}
}
@proceedings {7276,
	title = {The impact of products exchange in multi-levels multi-products distribution network},
	journal = {Second International Conference on Embedded \& Distributed Systems (EDiS)},
	year = {2020},
	address = {Oran, Algeria},
	abstract = {
	In this paper we analyze a problem of inventory management in a multi-levels multi-products distribution network with three echelon, the studied system consists of a central warehouse and three distribution centers identified by their location zones where each center is connected to a wholesaler group that serve the retailers of his region, which in turn feeds the customers of the regions located in the Algerian territory. The aim of this study is to apply a collaboration between the different actors of the same level in a form of an exchange of products, the exchange can occurs only when the actual demand is being received, in order to study the impact of product exchanges in the distribution networks and its influence on the total costs of the logistics chain from the central warehouse to the delivery to the final customer.

},
	url = {https://ieeexplore.ieee.org/document/9296441},
	author = {Benfriha, Abdennour -Ilyas and Triqui-Sari, Lamia and Bougloula, Aimade-Eddine and BENNEKROUF, Mohammed}
}
@proceedings {7275,
	title = {Minimizing energy consumption in a Job Shop problem with unidirectional transport constraint},
	journal = {13th International Colloquium of Logistics and Supply Chain Management (LOGISTIQUA)},
	year = {2020},
	address = {Fez, Morocco},
	abstract = {
	In this work, we introduce the objective of minimizing energy consumption in a job shop scheduling problem with unidirectional transport constraint. In this problem, it is planned to process a set of N jobs (parts) on four machines. The Movement of jobs between these machines is in a single direction that is mean all the parts follow the same direction of movement. Indeed, the energy consumption in this type of problem depends; on the one hand on the speed of the machines processing the jobs and on the other hand on the speed of the means of transport. To solve this optimization problem, we have proposed a metaheuristic method that allows us to find a better sequencing of jobs in order to minimize the cost generated by energy consumption. Several simulations have been studied and the results obtained demonstrate the effectiveness of the proposed approach.

},
	author = {Hadri, Abdelkader and Belkaid, Fay{\c c}al and Bougloula, Aimade-Eddine}
}
@conference {7274,
	title = {Efficient Approach for Parallel Machine Scheduling Problem},
	booktitle = {International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA 2018)},
	year = {2020},
	address = {Tangier, Morocco},
	abstract = {
	In this paper, we consider a parallel machine scheduling problem with non-renewable resources. Each job consumes several components and must be processed in one stage composed of identical parallel machines. Resources availability operations, jobs assignment and sequencing are considered and optimized simultaneously. In order to find an optimal solution, an exact method is applied to optimize the total completion time. Due to the problem complexity and prohibitive computational time to obtain an exact solution, a metaheuristic approach based genetic algorithm is proposed and several heuristics are adapted to solve it. Moreover, the impact of non-renewable resources procurement methods on production scheduling is analyzed. The system performances are evaluated in terms of measures such as the solution quality and the execution time. The simulation results show that the proposed genetic algorithm gives the same results as the exact method for small instances and performs the best compared to heuristics for medium and large instances.

},
	url = {https://ieeexplore.ieee.org/document/8428302},
	author = {Belkaid, Fay{\c c}al and Hadri, Abdelkader and BENNEKROUF, Mohammed}
}
@proceedings {7266,
	title = {Analysis of Human Skills in Industry 4.0},
	journal = {The Twelfth International Conference on Information, Process, and Knowledge Management (eKNOW 2020)},
	year = {2020},
	abstract = {
	This paper presents a state-of-the-art of recent research work analyzing the requirements of Industry 4.0, particularly related to the competences issue. Over the last few years, the fourth industrial revolution has attracted researchers worldwide to find suitable solutions. However, there are still many gaps related to the Industry 4.0, particularly related to the humans competences issue. Among the many challenges facing companies in this paradigm, one of the most important is the qualification of employees with the necessary skills to succeed in a transformed work environment. To cope with knowledge and competence challenges related to new technologies and processes of Industry 4.0, new strategic approaches for holistic human resource management are needed in manufacturing companies. The main objective of the presented research is to investigate the importance of employee competences, key to the development of Industry 4.0

},
	url = {file:///C:/Users/VRPG/Downloads/eknow_2020_1_40_68008.pdf},
	author = {Fadhila, Djouggane and Samia Aitouche and Aksa,  Karima}
}
@proceedings {7265,
	title = {A Scientometric Framework: Application for Knowledge Management (KM) in Industry Between 2014 and 2019},
	journal = {The Twelfth International Conference on Information, Process, and Knowledge Management (eKNOW 2020)},
	year = {2020},
	abstract = {
	It is always difficult to identify the most recent works that have been published, especially those published in recent years, due to delays in putting publications online, citations indexe, etc. Scientometry offers to researchers various concepts, models and techniques that can be applied to knowledge management (KM) in order to explore its foundations, its state, its intellectual core, and its potential future development. To this end, we have developed a scientometric KM framework to calculate the scientometric indexes related to a query introduced in the Scopus database, to facilitate research and monitoring of productivity and collaboration between the authors of KM in particular and also the dissemination of knowledge. The works between 2014 and 2019 are taken, the industry of services was omitted. It might help the decision makers and researchers to optimize their time and efforts. We used Unified Modeling Language (UML) to translate the development ideas of the scientometric framework structure into diagrams, and Delphi 7 to calculate the indexes and ensure other operations of research (about: articles, their authors, conferences, etc). This framework is only valid for Excel files extracted from Scopus or similar format. Finally, the relation between KM and industry 4.0 was established on found articles in Scopus.

},
	url = {http://personales.upv.es/thinkmind/dl/conferences/eknow/eknow_2020/eknow_2020_1_10_68003.pdf},
	author = {Samia Aitouche and Sahraoui, Khaoula and Aksa,  Karima and Djouggane, Fadhila and Cherrid, Walid and Belayati, Souleyman}
}
@proceedings {7264,
	title = {Application of Data Mining in Industry in the Transition Era to Industry 4.0: Review},
	journal = {The Twelfth International Conference on Information, Process, and Knowledge Management (eKNOW 2020)},
	year = {2020},
	abstract = {
	The era of Industry 4.0 has already begun, however, several improvements should be achieved concerning this revolution. Data mining is one of the modest and efficient tools. Based on a specific query entered in Scopus, related to Industry 4.0, data mining (DM) and logistics, selected documents were studied and analyzed. A brief background of Industry 4.0 and DM are presented. A generic analysis showed that the attentiveness for the cited subject area by countries, universities, authors and especially companies and manufacturers increased through the years. Content analysis reveals that the improvement in quality of the technologies used in manufacturing was noticed, concluding that DM would give Industry 4.0 a leap forward, yet research is dealing with several challenges.

},
	url = {file:///C:/Users/VRPG/Downloads/eknow_2020_1_30_68006.pdf},
	author = {Sahraoui, Khaoula and Samia Aitouche and Aksa,  Karima}
}
@proceedings {7263,
	title = {Post Prognostic Decision for Predictive Maintenance Planning with Remaining Useful Life Uncertainty},
	journal = {Prognostics and Health Management Conference (PHM-Besan{\c c}on)},
	year = {2020},
	address = {Besancon, France},
	abstract = {
	This paper investigates the use of the Particle Swarm Optimization (PSO) algorithm to quantify the effect of RUL uncertainty on predictive maintenance planning. The prediction of RUL is influenced by many sources of uncertainty, and it is required to quantify their combined impact by incorporating the RUL uncertainty in the optimization process to minimize the total maintenance cost. In this work, predictive maintenance of a multi-functional single machine problem is adopted to study the impact of RUL uncertainty on maintenance planning. Therefore, the PSO algorithm is integrated with a random sampling-based strategy to select a sequence that performs better for different values of RUL associated with different jobs. Through a numerical example, results show the importance of optimizing maintenance actions under the consideration of RUL randomness.

},
	url = {https://ieeexplore.ieee.org/document/9115558},
	author = {Benaggoune, Khaled and Meraghni, Safa and Ma, Jian and Leila-Hayet Mouss and Zerhouni, Noureddine}
}
@proceedings {7262,
	title = {Industrial supervision system based on machine learning SVM technique},
	journal = {International Conference on Robotics, Machine Learning and Artificial Intelligence (ICRMLAI),06 february },
	year = {2020},
	address = { Istanbul, Turkey},
	author = {Hanane Zermane and Leila-Hayet Mouss and Touahar, Djamel}
}
@proceedings {7240,
	title = {Global Solar Radiation Forecasting With Evolutionary Autoregressive Models},
	journal = {4th International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES{\textquoteright}20)},
	year = {2020},
	abstract = {
	Nowadays, the integration of solar power into the electrical grids is vital to increase energy efficiency and profitability. Effective usage of the instable solar production of photovoltaic (PV) systems necessitates trustworthy forecasting information. Actually, this addition can gives an ameliorated service quality if the solar radiation variation can be forecasted accurately. In this paper, we propose a new forecasting approach that integrates Autoregressive Moving Average (ARMA) and Genetic algorithms (GA) to make benefit of both of them in order to forecast Global Horizontal Irradiance (GHI) component. The proposed approach is compared with the standard ARMA model. The experimental results show that, the proposed approach outperforms the classical ARMA models in terms of mean absolute percentage error (MAPE), root mean squared error (RMSE) coefficient of determination (R)2 and the normalized mean squared error (NMSE).

},
	url = {https://www.researchgate.net/publication/349636080_Global_Solar_Radiation_Forecasting_With_Evolutionary_Autoregressive_Models},
	author = {Zemouri, Nahed and Bouzgou, Hassen and Gueymard, Chris}
}
@proceedings {7239,
	title = {Regularization Based Particle Swarm Optimization for Length Changeable Extreme Learning Machine under Health State Estimation of Military Aircraft Engines},
	journal = {8thINTERNATIONAL CONFERENCEON DEFENSESYSTEMS: ARCHITECTURES AND TECHNOLOGIES (DAT{\textquoteright}2020) April14-16,},
	year = {2020},
	address = {Algiers, Algeria},
	abstract = {
	In this work a new data-driven approach for Remaining Useful Life estimation of aircraft engines is developed. The proposed approach is a regularized Single Hidden Layer Feedforward Neural network (SLFN) with incremental constructive enhancements. The training rules of this algorithm are inspired form different Extreme Learning Machine (ELM) variants. Particle Swarm Optimization (PSO) algorithm is integrated to enhance tracking ability of the best regularization parameter to reduce the norm of the tuned weights. The proposed approach is evaluated using C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset and compared to its other derivatives and proved its accuracy. C-MAPSS software has revisions in military and civil applications. In this paper, the military version of its application is the used one.

},
	url = {https://www.researchgate.net/publication/340732320_Regularization_Based_Particle_Swarm_Optimization_for_Length_Changeable_Extreme_Learning_Machine_under_Health_State_Estimation_of_Military_Aircraft_Engines},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Ouahab Kadri}
}
@proceedings {7238,
	title = {Adaptive Sparse On-line Sequential Autoencoder for Sensors Measurements Compression Applied to Military Aircraft Engines},
	journal = {8thINTERNATIONAL CONFERENCEON DEFENSESYSTEMS: ARCHITECTURES AND TECHNOLOGIES (DAT{\textquoteright}2020) April14-16},
	year = {2020},
	address = {Algiers, Algeria},
	abstract = {
	In this work a new data-driven compression approach is presented. The compression algorithm is an autoencoder trained with an improved On-line sequential Extreme Learning Machine (OS-ELM). First, a dynamic adaptation of the training algorithm towards the newly coming data is achieved by integrating an updated selection strategy (USS) and dynamic forgetting function (DDF). Second, Singular Value Decomposition (SVD) is involved to enhance hidden layer representation via sparse mapping. This new developed autoencoder (ASOS- AE) is compared with the ordinary OS-ELM autoencoder (OS-AE) and proved its accuracy in CMAPSS dataset (Commercial Modular Aero-Propulsion System Simulation). The C-MAPSS software has revisions in civil and military applications. In the present work we used the military version of its applications.

},
	url = {https://www.researchgate.net/publication/340732102_Adaptive_Sparse_On-line_Sequential_Autoencoder_for_Sensors_Measurements_Compression_Applied_to_Military_Aircraft_Engines},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Ouahab Kadri}
}
@proceedings {7237,
	title = {Remaining Useful Life Prediction for aircraft engines with a new Denoising On-Line Sequential Extreme Learning Machine with Double Dynamic Forgetting Factors and Update Selection Strategy},
	journal = {12th Conference on Mechanical Engineering March 17-18, 2020 Ecole Militaire Polytechnique Bordj El Bahri},
	year = {2020},
	address = {Algiers, Algeria},
	url = {https://www.researchgate.net/publication/341509226_Remaining_Useful_Life_Prediction_for_aircraft_engines_with_a_new_Denoising_On-Line_Sequential_Extreme_Learning_Machine_with_Double_Dynamic_Forgetting_Factors_and_Update_Selection_Strategy},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Ouahab Kadri}
}
@proceedings {7236,
	title = {Dynamic Adaptation for Length Changeable Weighted Extreme Learning Machine},
	journal = {International conferance of intelligent},
	year = {2020},
	address = {Amsterdam,  The  Netherland},
	abstract = {
	In this paper, a new length changeable extreme learning machine is proposed. The aim of the proposed method is to improve the learning performances of a Single hidden layer feedforward neural network (SLFN) under rich dynamic imbalanced data. Particle Swarm Optimization (PSO) is involved for hyper-parameters tuning and updating during incremental learning. The algorithm is evaluated using a subset from C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset of gas turbine engine and compared to its derivatives. The results prove that the new algorithm has a better learning attitude. The toolbox that contains the developed algorithms of this comparative study is publicly available.

},
	url = {https://www.researchgate.net/publication/337945405_Dynamic_Adaptation_for_Length_Changeable_Weighted_Extreme_Learning_Machine},
	author = {Berghout, Tarek and Leila-Hayet Mouss and Ouahab Kadri}
}
@proceedings {7235,
	title = {Regularized Length Changeable Extreme Learning Machine with Incremental Learning Enhancements for Remaining Useful Life Prediction of Aircraft Engines},
	journal = {1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), 16-17 May},
	year = {2020},
	address = {El Oued, Algeria},
	abstract = {
	The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.

},
	url = {https://ieeexplore.ieee.org/document/9151607},
	author = {Berghout, Tarek and Leila-Hayet Mouss}
}
@article {7232,
	title = {An Indoor Tutorial For Maintenance And Production: Case Of Textile Batna},
	journal = {khazzartech الاقتصاد الصناعي},
	volume = {10},
	number = {2},
	year = {2020},
	pages = {216-231},
	abstract = {
	Communication and teamwork are among the most recurrent skills associated with knowledge of engineering sciences. However, their application is not simple, due to the lack of a pedagogical approach that contributes to the development of knowledge based on experience. The problem in factories is the lack of daily self learning to avoid the essential presence of the experts in to resolve problems. In this work, we defined what is a learning organization, what is a tutorial and why a personalized tutorial in a trade, its different forms and steps for the development of a tutorial. After we gave a presentation of the company that is Textile Batna. This article discusses how to design a personalized tutorial, oriented and aimed at learning and knowledge transfer in the industry. By developing this system we aim to build an experimental database serving to preserve the knowledge of the production industry expertise of the Batna textile factory. We have designed a tutorial for the company in the form of a website. For this, the UML language was used. The tutorial features were presented. It helped employees to aquire certain skills without assistance of experts.

},
	url = {https://www.asjp.cerist.dz/en/article/141068},
	author = {Ag Hameyni, Abdoulmadjid and Samia Aitouche and Taouririt, Kamel and Aksa,  Karima}
}
@article {7221,
	title = {Determination and Classification of Explosive Atmosphere Zones While Considering the Height of Discharges},
	journal = {Journal of Failure Analysis and Prevention },
	volume = {20},
	year = {2020},
	pages = {503{\textendash}512},
	abstract = {
	Prevention and protection of explosions are two notions often used subjectively, and to transform them into operative terms of decision support, it is indispensable to develop quantitative or semiquantitative approaches to determine the hazardous zones. The {\textquotedblleft}classical and point-source{\textquotedblright} approaches that determine ATEX (explosive atmospheres) zones are semiquantitative methods that can meet the requirements of the ATEX directives (Directives 99/92/EC and 94/9/EC). The methodology{\textquoteright}s principle in determining ATEX zones consists in making a comparison with typical examples {\textquotedblleft}classical approach{\textquotedblright} and to identify the source points, determine the degree of discharge, identify the type of the zone, determine the radius of the zone and ultimately the extent and shape of this zone {\textquotedblleft}source point approach.{\textquotedblright} The aim of this work is, on the one hand, to propose and present a classification methodology of the ATEX zones and, on the other hand, to apply the proposed methodology in a hydrocarbon separator.

},
	url = {https://link.springer.com/article/10.1007/s11668-020-00851-8},
	author = {Mihoub, Zakarya and Ouslati, Ahmed and Hacene Smadi and May, Bilal}
}
@article {7213,
	title = {Enhancement of the competitiveness and the financial capability of a manufacturing process through a new value stream mapping approach},
	journal = {International Journal of Productivity and Quality Management},
	volume = {29},
	number = {4},
	year = {2020},
	abstract = {
	The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJPQM.2020.106399},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mouss, Mohammed-Djamel}
}
@article {7212,
	title = {Enhancement of value stream mapping application process through using fuzzy DEMATEL and fuzzy QFD approaches: a case study considering economic and environmental perspectives},
	volume = {16},
	number = {3},
	year = {2020},
	abstract = {
	Purpose



	
		This paper aims to investigate an integrated approach that aims at enhancing the application process of value stream mapping (VSM) method. It also proposes an extended VSM called Economic and Environmental VSM(E-EVSM). The proposed approach highlights the improvement of economic and environmental performances.
	



	Design/methodology/approach



	
		The proposed approach has studied the integration of VSM, fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy quality function deployment (QFD) to improve the economic and environmental performances of manufacturing processes. The VSM method is used for data collection and manufacturing process assessment, whereas fuzzy DEMATEL is used to analyse the current state map. Finally, fuzzy QFD is used to organize the improvement phase of VSM method.
	



	Findings



	
		The clear findings of this research prove the effectiveness of VSM method on the environmental and economic performances of manufacturing processes. In addition, the proposed approach will show the advantages of fuzzy DEMATEL and fuzzy QFD approaches in improving the application of the VSM method.
	



	Research limitations/implications



	
		The limitation of this study includes the lack of consideration of other dimensions such as social, technological and managerial. In addition, the proposed approach studied an average set of environmental and economic indicators.
	



	Originality/value



	
		The novelty of the proposed approach is proved by the development of an extended VSM method (E-EVSM). Also, the proposed approach contributes by a new methodology for analysing and improving the current state map of manufacturing processes.
	

},
	url = {Journal of Modelling in Management},
	author = {Aouag, Hichem and Mohyiddine Soltani and Mouss, Mohamed-Djamel}
}
@article {7211,
	title = {HARDWARE IMPLEMENTATION USING XSG OF NEW FAULT DETECTION METHOD APPLIED TO ROBOT MANIPULATOR},
	journal = {Mechatronic Systems and Control },
	year = {2020},
	abstract = {
	This paper presents a new hardware implementation of a supervision system used in robot manipulators with two degrees of freedom. In addition to the simulation results, the new System Generator tool of Xilinx r is used to ensure self-generation of HDL codes. This code is used to configure field programmable gate arrays (FPGA) devices in the loop, and the supervision system is used mainly to ensure real-time reconfiguration of robots. In the proposed system, we used a new fault detection (FD) method for a viscous friction fault in the supervised robot combined with a fault-tolerance control method. The first module, based on residual analysis, is used to FD and to properly estimate the necessary corrections of the second module. For data transmission between the supervisor and the supervised robots, we used an approach based on the transmission control protocol. The simulation results show that the proposed method adjusts the fault effect using information transferred from the remote supervisor robot. The hardware implementation generated using Xilinx r System Generator is used to validate the proposed contribution and to ensure real-time processing in the case of industrial robots. The simulation results and the response times of both proposed systems are compared and discussed.

},
	url = {https://www.actapress.com/Abstract.aspx?paperId=47131},
	author = {Ghrieb, Abdel-O and Kourd, Yahia and Messaoudi, Kamel and Mouss, Mohamed-Djamel and Bakir, Toufik}
}
@article {7209,
	title = {DIGITAL LEARNING WITH COVID-19 IN ALGERIA},
	journal = {INTERNATIONAL JOURNAL OF 3D PRINTING TECHNOLOGIES AND DIGITAL INDUSTRY},
	volume = {4},
	number = {2},
	year = {2020},
	pages = { 161-170},
	abstract = {
	The coronavirus (COVID-19) pandemic poses an unprecedented global challenge, impacting profoundly on health and wellbeing, daily life, and the economy around the world. The COVID-19 pandemic has also changed education forever. The COVID-19 has resulted in schools shut all across the world. Globally, all children at schools or students at universities are out of the classroom. As a result, education has changed dramatically, with the notable rise of e-learning, whereby teaching is undertaken remotely and on digital platforms. Batna 2 University -situated in East of Algeria- is one of the universities suggested after the spread of COVID-19 in March, that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. All institutes and departments, including the Industrial Engineering department, are started using the e-learning Moodle platform to publish courses for all degrees of study and establish online sessions, especially for Ph.D. students.

},
	url = {https://dergipark.org.tr/en/download/article-file/1237519},
	author = {Hanane Zermane and Samia Aitouche}
}
@article {7207,
	title = {An integrated framework using VSM, AHP and TOPSIS for simplifying the sustainability improvement process in a complex manufacturing process},
	journal = {Journal of Engineering, Design and Technology},
	volume = {18},
	number = {1},
	year = {2020},
	abstract = {
	Purpose



	
		The purpose of this paper is to propose an integrated approach for assessing the sustainability of production and simplifying the improvement tasks in complex manufacturing processes.
	



	Design/methodology/approach



	
		The proposed approach has been investigated the integration of value stream mapping (VSM), analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). VSM is used as a basic structure for assessing and improving the sustainability of the manufacturing process. AHP is used for weighting the sustainability indicators and TOPSIS for prioritizing the operations of a manufacturing process regarding the improvement side.
	



	Findings



	
		The results carried out from this study help the managers{\textquoteright} staff in organizing the improvement phase in the complex manufacturing processes through computing the importance degree of each indicator and determining the most influential operations on the production.
	



	Research limitations/implications



	
		The major limitations of this paper are that one case study was considered. In addition, to an average set of sustainability indicators that have been treated.
	



	Originality/value



	
		The novelty of this research is expressed by the development of an extended VSM in complex manufacturing processes. In addition, the proposed approach contributes with a new improvement strategy through integrating the multi-criteria decision approaches with VSM method to solve the complexity of the improvement process from sustainability viewpoints.
	

},
	url = {https://www.emerald.com/insight/content/doi/10.1108/JEDT-09-2018-0166/full/html},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mohamed Djamel Mouss}
}
@article {7206,
	title = {Enhancement of the competitiveness and the financial capability of a manufacturing process through a new value stream mapping approach},
	journal = {International Journal of Productivity and Quality Management},
	volume = {29},
	number = {4},
	year = {2020},
	abstract = {
	The organisations having a futuristic look and aiming to impose their presence in the industrial field for a long possible term, are seeking for finding solutions linked to controlling their cash flow and assessing their competitiveness performances. Therefore, the purpose of this paper is to propose a new quality and cost value stream mapping for monitoring the costs consumption and assessing the competitiveness of a company. We use three key concepts namely life cycle costing for estimation of the most influential costs on the manufacturing process, the weighted DPMO and Sigma level for assessing the quality level and the competitiveness of the company. Finally, the data obtained are mapped using value stream mapping method for enabling the determination of dysfunctions in the cost and quality context.

},
	url = {https://www.inderscienceonline.com/doi/abs/10.1504/IJPQM.2020.106399},
	author = {Mohyiddine Soltani and Aouag, Hichem and Mouss, Mohammed-Djamel}
}
@article {7189,
	title = {HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST},
	journal = {ACADEMIC JOURNAL OF MANUFACTURING ENGINEERING},
	volume = {18},
	number = {3},
	year = {2020},
	abstract = {
	The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

},
	url = {https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf},
	author = {Adel Abdelhadi and Leila-Hayet Mouss and Ouahab Kadri}
}
@article {7176,
	title = {A new approach agent-based for distributing association rules by business to improve decision process in ERP systems},
	journal = {International Journal of Information and Decision Sciences},
	volume = {12},
	number = {1},
	year = {2020},
	abstract = {
	Nowadays, the distributed computing plays an important role in the data mining process. To make systems scalable it is important to develop mechanisms that distribute the workload among several sites in a flexible way. Moreover, the acronym ERP refers to the systems and software packages used by organisations to manage day-by-day business activities. ERP systems are designed for the defined schema that usually has a common database. In this paper, we present a collaborative multi-agent based system for association rules mining from distributed databases. In our proposed approach, we combine the multi-agent system with association rules as a data mining technique to build a model that can execute the association rules mining in a parallel and distributed way from the centralised ERP database. The autonomous agents used to provide a generic and scalable platform. This will help business decision-makers to take the right decisions and provide a perfect response time using multi-agent system. The platform has been compared with the classic association rules algorithms and has proved to be more efficient and more scalable.

},
	url = {https://www.inderscience.com/info/inarticle.php?artid=104993},
	author = {Zoubeidi, Merouane and Kazar, Okba and Benharzallah, Saber and Mesbahi, Nadjib and Merizig, Abdelhak and Rezki, Djamil}
}
@article {7175,
	title = {HYBRID MULTI-AGENT AND IMMUNE ALGORITHM APPROACH TO HYBRID FLOW SHOPS SCHEDULING WITH SDST},
	journal = {https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf},
	volume = {18},
	number = {3},
	year = {2020},
	abstract = {
	The existing literature on process scheduling issues have either ignored installation times or assumed that installation times on all machines is free by association with the task sequence. This working arrangement addresses hybrid flow shop scheduling issues under which there are sequence-dependent configuration times referred to as HFS with SDST. This family of production systems are common in industries such as biological printed circuit boards, metallurgy and vehicles and automobiles making. Due to the increasing complexity of industrialized sectors, simple planning systems have failed to create a realistic industrial scheduling. Therefore, a hybrid multi-agent and immune algorithm can be used as an alternative approach to solve complex problems and produce an efficient industrial schedule in a timely manner. We propose in this paper a multi-agent and immune hybrid algorithms for scheduling HFS with SDST. The findings of this paper suggest that the proposed algorithm outperforms some of the existing ones including PSO (particle swarm optimization), GA (Genetic Algorithm), LSA (Local Search Algorithm) and NEHH (Nawaz Enscore and Ham).

},
	url = {https://www.ajme.ro/PDF_AJME_2020_3/L15.pdf},
	author = {Adel Abdelhadi and Leila-Hayet Mouss and Ouahab Kadri}
}
@article {7174,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	journal = {ICT for an Inclusive World},
	year = {2020},
	abstract = {
	This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-34269-2_37},
	author = {Rezki, Djamil and Leila Hayet Mouss and Baaziz,  Abdelkader and Rezki, Nafissa}
}
@article {7173,
	title = {User behaviour-based approach to define mobility devices needs of disabled person in Algeria: a questionnaire study},
	journal = {Disability and Rehabilitation: Assistive Technology},
	volume = {17},
	number = {4},
	year = {2020},
	pages = { 453-461},
	abstract = {
	This article showcases the adaptability of existing mobility devices for the Algerian disabled population. It aims to develop a behavior model of disabled Algerian persons through (1) development of a theoretical model based on literature review and (2) improvement of this model by using local collected data from our developed questionnaire.

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/17483107.2020.1791263},
	author = {Bellal, Salah-Eddine and Leila-Hayet Mouss and Sahnoun, M{\textquoteright}hammed and Messaadia, Mourad}
}
@article {7172,
	title = {Complex network to enhance characterization analysis in modelling product development process},
	journal = {African Journal of Science, Technology, Innovation and Development },
	volume = {21},
	number = {7},
	year = {2020},
	pages = {797-811},
	abstract = {
	Nowadays, successful and innovative product development is highly correlated with the company{\textquoteright}s success and reason for existence. A development process is a major factor influencing cost, timing and quality of product development. It requires additional attention to decisions made about programme, budget, technical and market risks. In this paper a product development process model is proposed in an innovation context and strategy framework of design process and project management. The process modelling is complex network theory based, to improve characterization analysis for product development process modelling. Required concepts for complex process are established to build product development mathematical model, and provide an overview of key definitions and complex networks advanced tools. Finally, a case study for an Algerian electric generator company is carried out to prove the practicality of the proposed model.

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/20421338.2020.1762355?journalCode=rajs20},
	author = {Bencherif, Fateh and Leila-Hayet Mouss}
}
@article {7171,
	title = {Holonic agent-based approach for system-level remaining useful life estimation with stochastic dependence },
	journal = {International Journal of Computer Integrated Manufacturing},
	volume = {33},
	number = {10},
	year = {2020},
	abstract = {
	The emerging behavior in complex systems is more complicated than the sum of the behaviors of their constituent parts. This behavior involves the propagation of faults between the parts and requires information about how the parts are related. Therefore, the prognostic function at the system-level becomes a very tough task. Conventional approaches focus on identifying faults and their probabilities of occurrence. In complex systems, this can create statistical limitations for prognostic function where component fault relies on the connected components in the system and their state of degradations. In this paper, a new Holonic agent-based approach is proposed for system-level remaining useful life (S-RUL) estimation with different dependencies. As the proposed approach can capture fault/failure mode propagation and interactions that occur in the system all the way up through the component and eventually system level, it can work as an automatic testing-tool in reliability tasks. Through a numerical example, the implementation is done in Java Agent Development Environment with and without consideration of stochastic dependence. Results show that the indirect effect of influencing components has a massive impact on the S-RUL, and the impact of stochastic dependencies should not be ignored, especially in the early stages of the system design.

},
	url = {https://www.tandfonline.com/doi/abs/10.1080/0951192X.2020.1795926?journalCode=tcim20},
	author = {Benaggoune, Khaled and Leila Hayet Mouss and A Abdessemed and Bensakhria, Mohamed}
}
@article {7170,
	title = {New fusion and selection approaches for estimating the remaining useful life using Gaussian process regression and induced ordered weighted averaging operators},
	journal = {Quality and Reliability Engenieering International Journal (QREIJ) },
	volume = {36},
	number = {6},
	year = {2020},
	pages = {2146-2169},
	abstract = {
	In this paper, we propose new fusion and selection approaches to accurately predict the remaining useful life. The fusion scheme is built upon the combination of outcomes delivered by an ensemble of Gaussian process regression models. Each regressor is characterized by its own covariance function and initial hyperparameters. In this context, we adopt the induced ordered weighted averaging as a fusion tool to achieve such combination. Two additional fusion techniques based on the simple averaging and the ordered weighted averaging operators besides a selection approach are implemented. The differences between adjacent elements of the raw data are used for training instead of the original values. Experimental results conducted on lithium-ion battery data report a significant improvement in the obtained results. This work may provide some insights regarding the development of efficient intelligent fusion alternatives for further prognostic advances.

},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/qre.2688},
	author = {Bouzenita, Mohammed and Leila-Hayet Mouss and Melgani, Farid and Toufik Bentrcia}
}
@article {7169,
	title = {Aircraft Engines Remaining Useful Life Prediction with an Improved Online Sequential Extreme Learning Machine},
	journal = {Appl. Sci},
	volume = {10},
	number = {3},
	year = {2020},
	abstract = {
	The efficient data investigation for fast and accurate remaining useful life prediction of aircraft engines can be considered as a very important task for maintenance operations. In this context, the key issue is how an appropriate investigation can be conducted for the extraction of important information from data-driven sequences in high dimensional space in order to guarantee a reliable conclusion. In this paper, a new data-driven learning scheme based on an online sequential extreme learning machine algorithm is proposed for remaining useful life prediction. Firstly, a new feature mapping technique based on stacked autoencoders is proposed to enhance features representations through an accurate reconstruction. In addition, to attempt into addressing dynamic programming based on environmental feedback, a new dynamic forgetting function based on the temporal difference of recursive learning is introduced to enhance dynamic tracking ability of newly coming data. Moreover, a new updated selection strategy was developed in order to discard the unwanted data sequences and to ensure the convergence of the training model parameters to their appropriate values. The proposed approach is validated on the C-MAPSS dataset where experimental results confirm that it yields satisfactory accuracy and efficiency of the prediction model compared to other existing methods.

},
	url = {https://www.mdpi.com/2076-3417/10/3/1062},
	author = {Berghout, Tarek and Le{\"\i}la-Hayet Mouss and Ouahab Kadri and Sa{\"\i}di, Lotfi and Mohamed Benbouzid}
}
@article {7168,
	title = {Intelligent Industrial Process Control Based on Fuzzy Logic and Machine Learning},
	journal = { International Journal of Fuzzy System Applications (IJFSA)},
	volume = {9},
	number = {1},
	year = {2020},
	abstract = {
	Manufacturing automation is a double-edged sword, on one hand, it increases productivity of production system, cost reduction, reliability, etc. However, on the other hand it increases the complexity of the system. This has led to the need of efficient solutions such as artificial techniques. Data and experiences are extracted from experts that usually rely on common sense when they solve problems. They also use vague and ambiguous terms. However, knowledge engineer would have difficulties providing a computer with the same level of understanding. To resolve this situation, this article proposed fuzzy logic to know how the authors can represent expert knowledge that uses fuzzy terms in supervising complex industrial processes as a first step. As a second step, adopting one of the powerful techniques of machine learning, which is Support Vector Machine (SVM), the authors want to classify data to determine state of the supervision system and learn how to supervise the process preserving habitual linguistic used by operators.

},
	url = {https://www.igi-global.com/article/intelligent-industrial-process-control-based-on-fuzzy-logic-and-machine-learning/245272},
	author = {Hanane Zermane and Kasmi, Rached}
}
@article {7167,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	journal = {Lecture Notes in Information Systems and Organisation},
	year = {2020},
	author = {Rezki, Djamil and Leila-Hayet Mouss and Baaziz,  Abdelkader and Rezki, Nafissa}
}
@book {7141,
	title = {Routage G{\'e}ographique Bas{\'e} sur le Syst{\`e}me de Coordonn{\'e}es Virtuelles},
	year = {2020},
	edition = { {\'E}ditions universitaires europ{\'e}ennes },
	abstract = {
	Ce livre pr{\'e}sente un syst{\`e}me de coordonn{\'e}es virtuelles appel{\'e} VCSCClockwise. Ce dernier est libre-GPS et sans points de r{\'e}f{\'e}rence aidant {\`a} cr{\'e}er les coordonn{\'e}es virtuelles. En se basant sur la notion de cluster, ce syst{\`e}me fonctionne parfaitement dans un r{\'e}seau homog{\`e}ne avec une forte connectivit{\'e} en produisant trois coordonn{\'e}es virtuelles uniques associ{\'e}es {\`a} chaque n{\oe}ud-capteur dans le r{\'e}seau. Declivity est un protocole de routage g{\'e}ographique utilis{\'e} pour tester et {\'e}valuer ce syst{\`e}me de coordonn{\'e}es virtuelles. Ce protocole peut choisir le chemin le plus court avec le minimum de dissipation de l{\textquoteright}{\'e}nergie. En plus, la livraison est toujours garantie que se soit dans un r{\'e}seau homog{\`e}ne avec une forte densit{\'e} ou non.

},
	author = {Aksa,  Karima}
}
@inbook {7140,
	title = {Rate of Penetration (ROP) Prediction in Oil Drilling Based on Ensemble Machine Learning},
	booktitle = {ICT for an Inclusive World},
	year = {2020},
	pages = {537-549},
	publisher = {Springer},
	organization = {Springer},
	abstract = {
	This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.

},
	url = {https://econpapers.repec.org/bookchap/sprlnichp/978-3-030-34269-2_5f37.htm},
	author = {Rezki, Djamil and Leila-Hayet Mouss and Baaziz,  Abdelkader and Rezki, Nafissa}
}
