El Habib Nfaoui

An Approach of Decision-Making Support Based on Collaborative Agents for Unexpected Rush Orders Management

Decisions at different levels of the supply chain can no longer be considered independently, since they may influence profitability throughout the supply chain. This paper focuses on the interest of multi-agent paradigm for the collaborative coordination in global distribution supply chain. Multi-agent computational environments are suitable for a broad class of coordination and negotiation issues involving multiple autonomous or semiautonomous problem solving contexts. An agent-based distributed architecture is proposed for better management of rush unexpected orders. This paper proposes a first architecture validated by a real and industrial case.

INTELLIGENT PRODUCT BASED ON MOBILE AGENT TO ACCELERATE THE NEW PRODUCT DEVELOPMENT PROCESS

To improve the ever-increasing demands products that are customized, all business activities performed along the product life cycle must be coordinated an d efficiently managed along the extended enterprise . For this, enterprise had wanted to retain control over the whole product lifecycle especially when the pro duct is in use/repair/recycling (End of Life phase). Althou gh there have been many previous research works about product lifecycle management in the Beginning of Li fe (BOL) and Middle of Life (MOL) phases, few addressed the End of Life (EOL) phase, in particula r. In this study, based on Auto-ID combined with mo bile multi-agent system technologies, we will try to imp rove innovation: (a) by minimize the lunch phase, ( b) and the involvement of the customer in product life cycle (voice of customer).

Implementing knowledge management in supply chain: Literature review

The current economic crisis combined with a hyper-competitive environment has developed a real need for optimization practices within supply chains. Realizing the importance of knowledge management (KM) in improving the supply chain (SC), knowledge management is a major enabler of supply chain management, and is a critical element in information intensive and multi-cultured enterprise environments [1]. The growing number of articles on application knowledge management in supply chain in the literature is an indication of the importance of this area and of its role in improving the competitiveness of an organization [2]. This paper summarizes several theoretical and methodological characteristics that have been developed recently to highlight the way in which knowledge management applications are proposed in the supply chain context. In particular, the paper focuses on three areas of research: Knowledge transfer, knowledge sharing and knowledge creation and learning.

A MOBILE AGENT BASED APPROACH FOR AUTOMATING "DISCOVER-COMPOSE" PROCESS OF SEMANTIC WEB SERVICES

Nowadays, the focus is not only on how to exchange data between companies but also on how to exchange services between them or between companies-customers in order to minimize IT charges and increase their profit. Thus, web services are an ad equate solution for the e-business thanks to their interoperability and reusability. The combination o f web services and semantic technology, which are called Semantic Web Services (SWS), allows their di scovery-composition-invocation process to be automatically performed by programs or intelligent agents. However, this process is still a challengin g task that includes several issues such as the compl exity of finding and composing distributed SWS. In this study we present a mobile agent based approach to discover and compose SWS in a distributed environment and extend some algorithms related to t his field. The article reports examples and experimental results in order to illustrate as well as to assess the benefits of the proposed approach .

Unsupervised topics extraction of microblogging posts: a DBpedia-based approach

Automatic extraction of topics has received a great attention in social web as many applications that process social data make use of this technique to extract the central ideas in social media posts. Moreover, these applications must extract entities, link them to entities in a knowledge-base and classify them into a set of topics. However, there are few systems that address the problems of linking and classification together, especially in the context of micro-posts. Furthermore, most of them are supervised. In this paper, we present a novel system for unsupervised topics extraction in micro-posts based on DBpedia which is a community effort to extract structured information from Wikipedia. Our approach leverages the taxonomic nature of DBpedia to process a given tweet with a hierarchical resolution. Finally, to show the effectiveness of our system we compare it with a well known system for social media text.

Multi-agents Architecture for Semantic Retrieving Video in Distributed Environment.

This paper presents an integrated multi-agents architecture for indexing and retrieving video information.The focus of our work is to elaborate an extensible approach that gathers a priori almost of the mandatory tools which palliate to the major intertwining problems raised in the whole process of the video lifecycle (classification, indexing and retrieval). In fact, effective and optimal retrieval video information needs a collaborative approach based on multimodal aspects. Clearly, it must to take into account the distributed aspect of the data sources, the adaptation of the contents, semantic annotation, personalized request and active feedback which constitute the backbone of a vigorous system which improve its performances in a smart way

A multi-objective approach for micro grid energy management based on fuzzy multi-agent decision-making process

In this paper, we present an improved fuzzy multi-agent decision-making process for micro grid energy management considering uncertainty and imprecision associated with renewable energy sources, electricity market price and load. This approach is proposed to solve a decision-making problem that implies economical/technical objectives which operates under different constraints. The main idea of this approach is promoting the agent global decision impact on the local decision made by each agent independently and separately for efficient power resources management operation. For this, a coordinator agent seeks to satisfy assigned objectives based on local decisions generated by fuzzy logic inference, using an indicative number to differentiate the priority of producing energy between units in order to support the final decision about the power that should be generated by each agent. Simulation results show that the proposed method is beneficial to handle the problem of MG energy management in flexible manner.

Mobile Agent architecture for intelligent Product to increase the effectiveness in End of Life Management

Sweep the entire product lifecycle information from design to recycling is the great challenge to overcome in manufacturing worldwide. Actually, managing product after-sales information is a key to innovation and launching new products that meet the customers‘ expectations. To address this objective, the current study proposes architecture for closing the product lifecycle information by using the emerging technologies (PEID) and Mobile Agent software. In this paper we focus on the End of Life (EOL), especially the after-sales stage. To this end, the proposed architecture aims enable product knowledge management (PKM) by gathering and analyzing product-related information after-sales and with the feedback to the previous phases for help actors to decision making process. Furthermore, we aim to enable the product data and knowledge management (PDKM). The proposed architecture uses the advent of hardware and software related to product identification technologies and multi-agent systems (MAS).