Constructing Intelligent Agents Using Java
Constructing Intelligent Agents Using Java
Application of multi-agent planning to the assignment problem
Computers in Industry
An overview of distributed process planning and its integration with scheduling
International Journal of Computer Applications in Technology
Experience and reflection on the development of a holonic job shop scheduling system
International Journal of Computer Applications in Technology
Cognitive communication in a multi-agent system for distributed process planning
International Journal of Computer Applications in Technology
UML 2.0 and agents: how to build agent-based systems with the new UML standard
Engineering Applications of Artificial Intelligence
Development of a multi-agent-based distributed simulation platform for semiconductor manufacturing
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The authors describe the implementation of a multi-agent system, whose goal is to enhance production planning i.e. to improve the construction of production orders. This task has been carried out traditionally by the module known as production activity control (PAC). However, classic PAC systems lack adaptive techniques and intelligent behaviour. As a result they are mostly unfit to handle the NP Hard combinatorial problem underlying the construction of right production orders. To overcome this situation, we illustrate how an intelligent and collaborative multi-agent system (MAS) obtains a correct production order by coordinating two different techniques to emulate intelligence. One technique is performed by a feed-forward neural network (FANN), which is embedded in a machine agent, the objective being to determine the appropriate machine in order to fulfil clients' requirements. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The entire MAS consists of a coordinator, a spy, and a scheduler. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. The scheduler agent programs the production orders. We achieve a realistic MAS that fully automates the construction and dispatch of valid production orders in a factory dedicated to produce labels.