Coordination techniques for distributed artificial intelligence
Foundations of distributed artificial intelligence
Coordinating Plans of Autonomous Agents
Coordinating Plans of Autonomous Agents
Decision Support Systems and Intelligent Systems
Decision Support Systems and Intelligent Systems
Distributed Manufacturing Scheduling Using Intelligent Agents
IEEE Intelligent Systems
Innovations in multi-agent systems
Journal of Network and Computer Applications
Ant colony intelligence in multi-agent dynamic manufacturing scheduling
Engineering Applications of Artificial Intelligence
JADE: A software framework for developing multi-agent applications. Lessons learned
Information and Software Technology
Agent-based decision support system for dynamic scheduling of a flexible manufacturing system
International Journal of Computer Applications in Technology
Intelligent production control decision support system for flexible assembly lines
Expert Systems with Applications: An International Journal
Agent-based distributed manufacturing control: A state-of-the-art survey
Engineering Applications of Artificial Intelligence
Supporting a multicriterion decision making and multi-agent negotiation in manufacturing systems
Intelligent Decision Technologies
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The main issue that is addressed in the current paper is a framework based on an efficient coordination for product design through evaluation, planning, and real-time monitoring by calculating the cost at the different stages. The proposed approach integrates agents into an Intelligent Decision Support System (IDSS) and presents a new type of multi-agent-based coordination engine in which the information exchange between agents is controlled via an efficient algorithm. The proposed system mainly includes six components: Resource Agent (RA), Planning Agent (PA), Performance Evaluation Agent (PEA), Database Management Agent (DMA), Rules and Criteria Selection Agent (RCSA) and User Interface Agent (UIA). The multi-agent simulation is used to allow agents to cooperate using an intelligent behavior, and to coordinate their goals and action plans in order to solve a problem. We use UVA methodology to calculate the production costs. This method provides the enterprise with new information on its performances, the profitability of its customers, markets, product, which will generate decisions in all business functions for a permanent progress. One objective of this work is to demonstrate that the UVA method is not only an accounting method of repartition but is also built on the choice of one measuring unit and one specific analysis approach. Experimental results demonstrate that the proposed IDSS could implement effective production control decision-making for solving the flow-shop manufacturing system. The study reports the basic design principles of the system as well as details of the application.