Abstract
The authors present a Multi-Agent System for constructing and releasing production orders. In a manufacturing enterprise, the generation of production orders consists in a set of coordinated tasks among departments. This has been achieved traditionally as a module of the Production Activity Control (PAC) system. However, classic PAC modules lack collaborative techniques and intelligent behaviour. Moreover, in real-life situations experienced planners take over traditional PAC systems, since the range of possibilities to actually build production orders increases exponentially. To contribute to production planning, we present an intelligent and collaborative Multi-Agent System (MAS), having coordinated two forms to emulate intelligence. The learning capability is achieved by means of a Feed-forward Artificial Neural Network (FANN) with the back-propagation algorithm. The FANN is embedded within a machine agent whose objective is to obtain the appropriate machine in order to comply with requirements coming from the sales department. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The MAS also consists of a coordinator and a spy. 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. Finally, a scheduler agent schedules the production orders. The resultant MAS improves the current form to plan production in a factory dedicated to produce labels.
Similar content being viewed by others
References
Bauer B., Odell J. (2005) UML 2.0 and agents: how to build agent-based systems with the new UML standard. Engineering Applications of Artificial Intelligence 18: 141–157
Bellifemine F.L., Caire G., Greenwood D. (2007) Developing Multi-Agent Systems with JADE. Wiley and Sons, USA
Bigus J., Bigus J. (2002) Constructing Intelligent Agents using Java . Wiley and Sons, NY
Browne, J., Harhen, J., & Shivnan, J. (1992). Production Management Systems. A CIM perspective. UK: Addison-Wesley.
Feng S.C. (2005) Preliminary design and manufacturing planning integration using web-based intelligent agents. Journal of Intelligent Manufacturing 16: 423–437
Feng S.C., Stouffer K.A., Jurrens K.K. (2005a) Manufacturing planning and predictive process model integration using software agents. Advanced Engineering Informatics 19: 135–142
Frey D., Nimis J., Worn H., Lockemann P. (2003) Benchmarking and robust multi-agent production planning and control. Engineering applications of Artificial Intelligence 16: 307–320
Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing. A computational approach to learning and machine intelligence. USA: Prentice-Hall.
Julka N., Srinivasan R., Karimi I. (2002) Agent-base supply chain management-1: framework. Computers and Chemical Engineering 26: 1755–1769
Kornienko S., Kornienko O., Priese J. (2004) Application of multi-agent planning to the assignment problem. Computers in Industry 54: 273–290
Lea B.R., Gupta M.C., Yu W.B. (2005) A prototype multi-agent ERP system: an integrated architecture and a conceptual framework. Technovation 25: 433–441
López-Morales V., López-Ortega O. (2005) A distributed semantic network model for a collaborative intelligent system. Journal of Intelligent Manufacturing 16: 515–525
López-Ortega O., López-Morales V. (2006) Cognitive communication in a multi-agent system for distributed process planning. International Journal of Computer Applications in Technology 26: 99–107
Marík V., Lazanský J. (2007) Industrial applications of agent technologies. Control Engineering Practice 15(11): 1364–1380
Paternina-Arboleda C.D., Das T.K. (2005) A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problems. Simulation Modelling Practice and Theory 13: 389–406
Russel S.J., Norvig P. (2002) Artificial intelligence: A modern approach (2nd ed). Prentice-Hall, USA
Symeonidis A.L., Kehagias D., Mitkas P. (2003) Intelligent policy recommendations on enterprise resource planning by the use of agent technology and data mining techniques. Expert Systems with Applications 25: 589–602
Walter S.S., Brennan R.W., Norrie D.H. (2006) Experience and reflection on the development of a holonic job-shop scheduling system. International Journal of Computer Applications in Technology 26: 15–27
Wang L., Shen W. (2003) DPP: An agent-based approach for distributed process planning. Journal of Intelligent Manufacturing 14: 429–439
Wang L., Shen W., Hao Q. (2006) An overview of distributed process planning and its integration with scheduling. International Journal of Computer Applications in Technology 26: 3–14
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
López-Ortega, O., López-Morales, V. & Villar-Medina, I. Intelligent and collaborative Multi-Agent System to generate and schedule production orders. J Intell Manuf 19, 677–687 (2008). https://doi.org/10.1007/s10845-008-0119-z
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-008-0119-z