http://dspace.univ-usto.dz/handle/123456789/600 2026-04-21T13:06:17Z 2026-04-21T13:06:17Z DJELID, Fatiha KHELIFA, Said MEKKAKIA MAAZA, Zoulikha http://dspace.univ-usto.dz/handle/123456789/602 2024-01-23T10:03:20Z 2024-01-23T00:00:00Z

Titre: Supply Models in Logistic Transport Networks Auteur(s): DJELID, Fatiha; KHELIFA, Said; MEKKAKIA MAAZA, Zoulikha Résumé: Supply management within the transport supply chain and logistics plays a pivotal role in shaping overall business performance and enhancing customer satisfaction. This article delves into a comprehensive examination of the supply models applied in this domain, giving due consideration to the diverse transport networks, which encompass road transport, rail, maritime, aerial, and multimodal transport. The objective is to review and synthesize various approaches and methodologies used in the existing scientific literature to address the diverse challenges of supply management in the transportation industry.

2024-01-23T00:00:00Z Leila, BENAISSA KADDAR Saïd, KHELIFA Mohamed El Mehdi, ZAREB http://dspace.univ-usto.dz/handle/123456789/601 2024-01-23T09:58:39Z 2024-01-23T00:00:00Z

Titre: Optimizing Industrial Process Control with Artificial Intelligence: A Case Study of the Feedback Procon Level and Flow Process Auteur(s): Leila, BENAISSA KADDAR; Saïd, KHELIFA; Mohamed El Mehdi, ZAREB Résumé: Artificial Intelligence (AI) techniques have brought about transformative changes across various industries, and the field of industrial process control is no exception. This paper presents an innovative approach centered around the development of an artificial neural network known as ANN-PID. This network emulates the decision-making process akin to that of a human operator for configuring parameters in a water Flow PID controller. At the heart of this approach lies a multi-layer perceptual neural network (MLP). The research entails training the ANN-PID model using real-time process data, aiming to optimize process parameters and elevate the efficacy of industrial process control. Through rigorous experimental tests conducted on the Feedback 38-003 Procon Level and Flow with Temperature process, the study confirms the prowess of the ANN-PID system in enhancing control performance when compared to traditional control methods. These findings represent a significant contribution to the evolution of intelligent control systems within industrial settings, ushering in novel opportunities for the automation and optimization of intricate processes.

2024-01-23T00:00:00Z