Publications by Year: 2016

2016

Yamina, Smaili. 2016. “Techniques d’Amélioration de la Traçabilité pour l’Optimisation d’une Chaîne Logistique”. JD'2016 Journées des doctorants, Batna, Algérie.

L, Guezzouli. 2016. “Un Système d’aide à la décision appliqué au GPS piloté par la voix humaine « cas de distribution de produits »”. JD'2016 Journées des doctorants, Batna, Algérie.

Z, Boukelkoul. 2016. “L’optimisation de l’extension d’un problème d’ordonnancement classique”. JD'2016 Journées des doctorants, Batna, Algérie.

O, Semassel. 2016. “Lot-sizing de plusieurs produits sur plusieurs lignes de production avec fenêtres de temps sur la production et sur les demandes”. JD'2016 Journées des doctorants, Batna, Algérie.

Assia, Laggoun. 2016. “Amélioration du problème de lot Sizing : Considérations Humaines et environnementales”. JD'2016 Journées des doctorants, Batna, Algérie.

Youcef, Boutarfa. 2016. “Lot-sizing avec optimisation des profits ("revenue management").”. JD'2016 Journées des doctorants, Batna, Algérie.

Walid, Mammeri. 2016. “Problème de planification de la production avec coordination et recyclage”. JD'2016 Journées des doctorants, Batna, Algérie.

Farid, Belbedj. 2016. “Intégration des problèmes de lot Sizing et d’ordonnancement dans les ateliers type Flow Shop”. JD'2016 Journées des doctorants, Batna, Algérie.

Amor, Belferhi. 2016. “L’apport des réseaux d’automates stochastiques pour la modélisation et l’évaluation des performances des systèmes de production a grand espace d’état”. JD'2016 Journées des doctorants, Batna, Algérie.

Athmane, Mechenene, and Aouag Hichem. 2016. 260 pages Mise en œuvre des outils de l’amélioration continue avec cas pratiques, ISBN-13: 978-3-8416-6281-1 ISBN-10: 3841662811. Editions universitaires européennes. Publisher's Version Abstract

La recherche de l'excellence industrielle est devenue aujourd'hui une condition essentielle de survie pour l'entreprise. Dans un environnement exigeant et concurrentiel, les entreprises doivent donc chercher en permanence à renforcer leur compétitivité. La réalisation de cet objectif nécessite le recours aux outils d'amélioration continue qui visent à booster la compétitivité et la performance des entreprises par le tryptique : réduction des couts, amélioration de la qualité et diminution des délais. Cet ouvrage se fixe pour objectif d'appliquer l'un des outils de l'amélioration continue, en l'occurrence la démarche six sigma, aux processus de fabrication d'une cimenterie algérienne (SCIMAT) et d'une entreprise de fabrication de bouteilles à gaz (B.A.G).

Athmane, Mechenene, and Aouag Hichem. 2016. 260 Mise en œuvre des outils de l’amélioration continue avec cas pratiques, ISBN-13: 978-3-8416-6281-1 ISBN-10: 3841662811. Editions universitaires européennes. Publisher's Version Abstract

Mohammed, Haoues, Dahane Mohammed, and Mouss Nadia Kenza. 2016. “Outsourcing optimization in two-echelon supply chain network under integrated production-maintenance constraints, ISSN /e-ISSN 0268-3768 / 1433-3015”. Journal of Intelligent Manufacturing Vol 30 (Issue 2) : 701–725. Publisher's Version Abstract

In this paper, we study a two-echelon supply chain network consisting of multi-outsourcers and multi-subcontractors. Each one is composed of a failure-prone production unit that produces a single product to fulfil market demands with variable production rates. Sometimes the manufacturing systems are not able to satisfy demand; in this case, outsourcing option is adopted to improve the limited in-house production capacity. The outsourcing is not justified by the production lack of manufacturing systems, but is also considered for the costs minimization issues. In the considered problem, we assume that the failure rate is dependent on the time and production rate. Preventive maintenance activities can be conducted to mitigate the deterioration effects, and minimal repairs are performed when unplanned failures occurs. We consider that the production cost depends on the rate of the machine utilization. The aim of this research is to propose a joint policy based on a mixed integer programming formulation to balance the trade-off between two-echelon of supply chain. We seek to assist outsourcers to determine the integrated in-house/ outsourcing, and maintenance plans, and the subcontractors to determine the integrated production-maintenance plans so that the benefit of the supply chain is maximized over a finite planning horizon. We develop an improved optimization procedure based on the genetic algorithms, and we discuss and conduct computational experiments to study the managerial insights for the developed framework.

Samira, Brahmi. 2016. “Measuring intellectual capital of an organization”. Advances in automotive technologies.

Nacereddine, Arrar. 2016. “Modélisation et évaluation des performances des systèmes logistiques”. JD'2017 Journées des doctorants, Batna, Algérie.

Mawloud, Titah. 2016. “Amélioration du processus de capitalisation et de partage des connaissances pour la maximisation de la valeur d’un système de production”. JD'2016 Journées des doctorants, Batna, Algérie.

Sami, Tkouti. 2016. “Conception et déploiement des systèmes de gestion de données techniques dans l’ingénierie de production”. JD'2016 Journées des doctorants, Batna, Algérie.

Aicha, Medgrou. 2016. “Optimisation de la navigation autonome d’un robot dans un espace de travail Industriel”. JD'2016 Journées des doctorants, Batna, Algérie.

Haroun, Belhoul. 2016. “Implantation d’une GMAO dans un système de production pour l’amélioration de la performance de l’entreprise”. JD'2016 Journées des doctorants, Batna, Algérie.

Said, NOUNE Med. 2016. “La politique de Sureté de Fonctionnement dans les systèmes industriels : Application aux stations d’épuration dites STEP”. JD'2016 Journées des doctorants, Batna, Algérie.

Zouina, AMEUR. 2016. “Contribution à l’utilisation des réseaux d’achat Electroniques (e-sourcing) en vue de l’optimisation des structures d’approvisionnements de l’algérienne des textiles”. JD'2016 Journées des doctorants, Batna, Algérie.

Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective

Benbouzid, Mohamed, and Tarek Berghout. 2023. “Quo Vadis Machine Learning-Based Systems Condition Prognosis?—A Perspective”. Electronics 12 (3) : 527. Publisher's Version Abstract

Data-driven prognostics and health management (PHM) is key to increasing the productivity of industrial processes through accurate maintenance planning. The increasing complexity of the systems themselves, in addition to cyber-physical connectivity, has brought too many challenges for the discipline. As a result, data complexity challenges have been pushed back to include more decentralized learning challenges. In this context, this perspective paper describes these challenges and provides future directions based on a relevant state-of-the-art review.

Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach

Berghout, Tarek, et al. 2023. “Lithium-ion Battery State of Health Prediction with a Robust Collaborative Augmented Hidden Layer Feedforward Neural Network Approach”. IEEE Transactions on Transportation Electrification. Publisher's Version Abstract

Lithium-ion (Li-ion) batteries play an important role in providing necessary energy when acting as a main or backup source of electricity. Indeed, the unavailability of battery aging discharge data in most real-world applications makes the State of Health (SoH) assessment very challenging. Alternatively, accelerated aging is therefore adopted to emulate the degradation process and to achieve an SoH estimate. However, accelerated aging generates limited deterioration patterns suffering from a higher level of complexity due to the non-linearity and non-stationarity imposed by harsh conditions. In this context, this paper aims to provide a predictive model capable of solving incomplete data problems by providing two main solutions for each of the problems of complexity and missing patterns, respectively. First, to overcome the problem of lack of patterns, a robust collaborative feature extractor (RCFE) is designed by collaborating between a set of improved restricted Boltzmann machines (I-RBMs) to be able to share learning knowledge among different locally trained I-RBMs to create a more generalized global extraction model. Second, a set of RCFEs is then evolved through a neural network with an augmented hidden layer (NAHL) to enhance the predictive ability by further exploring representation learning to overcome pattern complexity issues. The designed RCFE-NAHL is trained to predict SoH using constant current (CC) discharge characteristics by implying multiple characteristics recorded through the constant voltage (CV) charging process as indicators of health. The proposed SoH prediction approach performances are evaluated on a set of battery life cycles from the well-known NASA database. In this context, the achieved results clearly highlight the higher accuracy and robustness of the proposed learning model.

2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT

Othmane, Friha, et al. 2023. “2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT”. Computers & Security 127. Publisher's Version Abstract

Advanced technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), underpin many of the innovations in Industry 4.0. However, the interconnectivity and open nature of such systems in smart industrial facilities can also be targeted and abused by malicious actors, which reinforces the importance of cyber security. In this paper, we present a secure, decentralized, and Differentially Private (DP) Federated Learning (FL)-based IDS (2DF-IDS), for securing smart industrial facilities. The proposed 2DF-IDS comprises three building blocks, namely: a key exchange protocol (for securing the communicated weights among all peers in the system), a differentially private gradient exchange scheme (achieve improved privacy of the FL approach), and a decentralized FL approach (that mitigates the single point of failure/attack risk associated with the aggregation server in the conventional FL approach). We evaluate our proposed system through detailed experiments using a real-world IoT/IIoT dataset, and the results show that the proposed 2DF-IDS system can identify different types of cyber attacks in an Industrial IoT system with high performance. For instance, the proposed system achieves comparable performance (94.37%) with the centralized learning approach (94.37%) and outperforms the FL-based approach (93.91%) in terms of accuracy. The proposed system is also shown to improve the overall performance by 12%, 13%, and 9% in terms of F1-score, recall, and precision, respectively, under strict privacy settings when compared to other competing FL-based IDS solutions.

Faulty Detection System Based on SPC and Machine Learning Techniques

Benrabah, Mohamed-Elamine, Ouahab Kadri, and Nadia-Kenza Mouss. 2023. “Faulty Detection System Based on SPC and Machine Learning Techniques”. Revue de l’Intelligence Artificielle : 969-977. Publisher's Version Abstract

Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.

IoT-based food traceability system: Architecture, technologies, applications, and future trends

Mehannaoui, Raouf, Kinza-Nadia Mouss, and Karima Aksa. 2023. “IoT-based food traceability system: Architecture, technologies, applications, and future trends”. Food Control 145. Publisher's Version Abstract

An effective Food Traceability System (FTS) in a Food Supply Chain (FSC) should adequately provide all necessary information to the consumer(s), meet the requirements of the relevant agencies, and improve food safety as well as consumer confidence. New information and communication technologies are rapidly advancing, especially after the emergence of the Internet of Things (IoT). Consequently, new food traceability systems have become mainly based on IoT. Many studies have been conducted on food traceability. They mainly focused on the practical implementation and theoretical concepts. Accordingly, various definitions, technologies, and principles have been proposed. The “traceability” concept has been defined in several ways and each new definition has tried to generalize its previous ones. Nevertheless, no standard definition has been reached. Furthermore, the architecture of IoT-based food traceability systems has not yet been standardized. Similarly, used technologies in this field have not been yet well classified. This article presents an analysis of the existing definitions of food traceability, and thus proposes a new one that aims to be simpler, general, and encompassing than the previous ones. We also propose, through this article, a new architecture for IoT-based food traceability systems as well as a new classification of technologies used in this context. We do not miss discussing the applications of different technologies and future trends in the field of IoT-based food traceability systems. Mainly, an FTS can make use of three types of technologies: Identification and Monitoring Technologies (IMT), Communication Technologies (CT), and Data Management Technologies (DMT). Improving a food traceability system requires the use of the best new technologies. There is a variety of promising technologies today to enhance FTS, such as fifth-generation (5G) mobile communication systems and distributed ledger technology (DLT).

Laboratoire D'automatique et Productique