A multi-agent system to construct production orders by employing an expert system and a neural network |
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| Authors: | Omar López-Ortega Israel Villar-Medina |
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| Affiliation: | 1. Faculty of Aerospace Engineering, Shenyang Aerospace University, Shenyang 110136, China;2. College of Information Science and Technology, Bohai University, Jinzhou 121013, China;1. School of Mathematical Sciences and Institute of Finance and Statistics, Nanjing Normal University, Nanjing, 210023 Jiangsu, China;2. Advanced Control Systems Laboratory, School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China;3. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073 Hubei, China;1. Department of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran;2. Faculty of Engineering, Kharazmi University, Mofatteh Avenue, PO Box 15719-14911, Tehran, Iran;3. Department of Mechanical and Aerospace Engineering, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran;4. School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran;5. Mechanical, Aerospace and Nuclear Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, USA;1. Department of Information Technology, Lee-Ming Institute of Technology, Taiwan;2. Department of Mechanical Engineering, Lunghwa University of Science and Technology, Taiwan;3. Department of Electrical Engineering, Fu-Jen Catholic University, Taiwan;4. Chung-Shan Institute of Science and Technology, Taiwan;1. Department of Applied Mathematics, Xidian University, No. 2 Taibai South Road, 710071 Xi’an, China;2. Department of Applied Mathematics, Xi’an University of Architecture and Technology, 710055 Xi’an, China |
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| Abstract: | 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. |
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