Production Management Modelling Based on MAS

International Journal of Automation and Computing 7(3), August 2010, 336-341 DOI: 10.1007/s11633-010-0512-x Production Management Modelling Based on MAS Li He 1 Zheng-Hao Wang 2 Ke-Long Zhang 3 1 School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, PRC 2 School of Science, Shenyang Jianzhu University, Shenyang 110168, PRC 3 Shenyang Songyang Paper Cup Co., Ltd., Shenyang 110033, PRC Abstract: Modelling based on multi-agent system (MAS) was built for the current production management and process of Shenyang Songyang Paper Cup Co., Ltd. It can transmit the information instantly via order agent (OA), manager agent (MA), production agent (PA), and service agent (SA), and realize information sharing. The PA is also built on MAS, and it includes two agents, task agent (TA), and resource agent (RA). It has been found that the modelling is superior to the old one. It can improve the working flow and production efficiency, and shorten the time of delivery. Keywords: Production management, multi-agent system (MAS), modelling, agent, information share. 1 Introduction With the high speed development of economy, the paper products become more and more important because they are safe, healthy, non-toxic, odorless, non-polluting, and biodegradable. They are widely used in the food and medicament packaging industry. The plastic throw-away cups have been replaced gradually by the paper throw-away cups. The expert predicts that the paper products will sweep the country and find their way into families in three years. Now, in most paper cups enterprises, the paper cups begin to be made after the sales department receives the order. After a few processes, design in the design department, printing in the printing house, die cutting, and shaping in the workshops, the paper cups are finished and delivered to the customers. This production flow has the following disadvantages: 1) Long design and manufacturing period From the beginning of the order to the finished cups, the period is usually 10 15 days. It cannot adapt to the fierce competition. 2) Low degree of information sharing The order will be passed for several times among the customer, sales department, design department, printing house, and workshops, which may be located at different cities or different places in the same city. It takes a long time to pass the information and often create a little misunderstanding during the passing. 3) Low customer satisfaction What the customers require is not only the cup size, but also design style, color, the kind of paper, and other characteristics that the customers are interested in. Even a little misunderstanding will lower the customer satisfaction. 2 Multi-agent system 2.1 Agent structure An agent is a new computing model in the artificial intelligence field and can continually perceive the change outside Manuscript received January 1, 2010; revised April 1, 2010 and itself, and can also give a corresponding action by itself. An agent can be looked as a black box and can perceive the environment via sensors and act on the environment via effectors. Agents can communicate and cooperate with each other and complete complicated tasks. Along with the development of computer network and communication, the technology about agent is not only a hotspot in artificial intelligence but also in information technology, manufacturing technology, and some other fields [1,2]. In programming, one agent realizes the mapping from perception to action by the following [3] : Function skeleton-agent (percept) return action Static: memory // agent memory Memory update-memory (memory, percept) Action choose-best-action (memory) Memory update-memory (memory, percept) Return action. 2.2 Multi-agent system structure A complex system may include one or more interactional agent systems. This system is named as multi-agent system (MAS). MAS denotes that one complex problem can be solved via cooperation, harmonization, and negotiation of two or more agents together. MAS is considered as the key technique to lower production cost, realize decentralized production and self-adaption, and resolve complicated process. Moreover, it is also a new methodology, which runs through each modern advanced manufacturing field, from the dynamic alliance, distributed intelligent manufacture system, enterprises integration, enterprise resource planning (ERP) to field control [4]. According to communication mode, there are three kinds of MAS, direct communication, federated communication and centralized communication [3] (see Fig. 1). 2.3 Contract net protocol In all the concurrent methods of agent, the contract net protocol (CNP), which was put forward by R. G. Smith in 1980 [5], is one of the most comprehensive methods. It follows the mechanism of the market of inviting public bidding-bidding-win bidding. According to biding value,

H. Li et al. / Production Management Modelling Based on MAS 337 tasks are distributed between agents (See Fig. 2). The dynamic, distributed, and self-adaptive problem of task distribution can be solved by the concerned agents negotiating and competing with each other. Its objective is trying to complete tasks with the optimization allocation of resources and cost. Remarks 1. 1) After the task is disassembled, each disassembled task will create a task agent (TA) automatically. 2) TA will fill out the invitation of bids automatically according to its own condition, and send it to resource agent (RA), and then wait for bidding. 3) Each RA, corresponding one piece of equipment, evaluates the task and estimates if it is qualified to this task after receiving the invitation of bids. For the qualified task, RA gives the bidding value and fills out the bidding document. Otherwise, it exits the bidding. 4) RA sends the bidding document to production agent (PA) and waits for the estimated bidding value. 5) PA assigns the task according to all the received available bidding documents. 6) PA sends the winning bidding message to RA and TA. 7) TA evaluates the winning bidding and records after receiving the message. 8) RA begins to machine after receiving the message. 9) If there is a dissent, PA will cancel this bidding and begin to renegotiate. The new bidding process will begin. 3 Modelling based on MAS 3.1 Function and structure of each agent The production management modeling described in this paper is built based on multi-agent system, aiming at the current production management and process of Shenyang Songyang Paper Cup Co., Ltd. The order agent (OA), manager agent (MA), PA, and service agent (SA) are built, and the production agent is also built based on MAS, which includes TA and resource agent (RA). Each agent defines its own rule according to its own task and characteristic and then puts it in the rule databases that are packaged in agents. The function and structure of each agent are the following: 1) MA MA, which is built on the server, is built to manage, harmonize, and control the whole system. It can get the information from other agents and resolve the conflict among agents. MA must record the registration information, identity (ID), address, and function of each agent. When adding a new agent or deleting an old one, MA will update the system in accordance with the information. In addition, MA can apperceive other agents at all times. MA has the ability of judgment, decision making, and gets the optimal compound mode. The flow of MA is shown in Fig. 3, and the structure of MA is as follows: Manager agent = information management service management production management order management Information management = received information registration information agent ID Service management = design style size color printing information Order management = received order published task finished order new order urgent order Production management received task resource name resource number resource condition Feedback information = order condition production condition service condition. Fig. 1 Essencial structure of MAS Fig. 2 The modelling of contract net

338 International Journal of Automation and Computing 7(3), August 2010 resource Task management = received task published task task condition new task urgent task Resource management = resource name resource number resource condition update resource Feedback information = task condition resource condition finished task Finished task = machining quality machining time machining cost Record = task number task name task content resource number and name. PA is built also based on MAS. Each agent also defines its own rule according to respective tasks and characteristics and keeps it in the rule databases packaged in agents. PA implements general scheduling and harmonizes the conflict between RA and TA (see Fig. 4). Fig. 3 Flow chart of MA 2) OA It receives the requirement information from customers. Analyzes the requirement and estimates enterprise s capability. It changes the requirement into order and transmits it to MA. The structure of OA is shown as follows: Order agent = customer information production information order information Customer management = registration name level suggestion Production information = name amount quality requirement function requirement delivery date Evaluate ability = possessing ability needed ability external cooperation ability Order information = received order canceled order urgent order Order record = number name content finished condition matter. 3) SA Its function is to deal with design style, color, text, and printing before shaping cups. SA receives the task information from MA and deals with the style according to customer requirements, and then transmits the file to the customer for identification about pattern, color, text, and so on. After confirmation by the customer, SA will begin to draw a sample for printing. At the same time, SA calculates the paper in demand and transmits the information to MA. 4) PA Its function is production schedule. PA can get the tasks and available resources according to the registration form. PA disassembles the task received by contract into task information and then transmits the task information to TA. At the same time, PA records the information to the task table. PA collects bidding value of each RA and form bidding table. Then, confirms the winning RA according to the bidding value and affords the machining contract made by contract table and the winning RA. The RA will be selected to manufacture cups. The structure of PA is shown as follows: Production agent = information management task management resource management Information management = received information registered task canceled task registered resource canceled Fig. 4 The modelling of PA based on MAS 5) RA It is built on client. After registration, RA begins to wait for inviting public bidding. When RA receives inviting public bidding document, it will evaluate the task according to production state and ability. Then, computes a feasible bidding value and fills in the bidding document and sends it to PA (see Fig. 5). Fig. 5 Flow chart of RA When RA receives the winning bidding message, the task will be scheduled on the corresponding machine. After finishing the task, RA will wait for the next bidding. Also,

H. Li et al. / Production Management Modelling Based on MAS 339 RA monitors and controls the machining process in real time. If the normal state is changed, for example, there is something wrong with certain machines or new machines are added, RA immediately reports it to PA, which will reschedule dynamically. The structure of OA is shown as follows: Resource agent = equipment name equipment information equipment condition Equipment information = name number condition machining size precision machining range Equipment condition = normal/fault have/no task Bidding = evaluated production capacity bidding document sign a contract Feedback information = win bidding message task execute Task execute = execution instruction monitor condition. 6) TA TA answers for executing the whole production plan. It creates automatically and will be vanished automatically after finishing one task. It can finish the distribution and execution of the plan via negotiation with each client and server. An incepting buffer and a sending buffer are packaged in each agent for communicating to other agents. Fig. 6 shows the flow chart of TA. Fig. 6 Flow chart of TA The structure of OA is as follows: Contract information = task information contract content delivery date Task information = task number task name task requirement Contract content = production name production size production requirement Time information = the beginning time of invitation for bids the ending time of invitation for bids Feedback information = bidding RA win the bidding agent machining condition. 3.2 Modeling based on MAS This modeling, based on multi-agent and the negotiation of contract net protocol (CNP), adopts the adopted client/server structure for communication. It realizes communication via the communication port of the numerical control system. RA and equipment are connected by the controller area network (CAN) bus [6], and data are transmitted two ways in numerical control equipment. The working flow is shown as follows: After OA receives the requirements from the customers, it begins to estimate if the enterprise has the capability to meet the requirements. For the unserviceable task, the sales department gives the customers practicable suggestions. For the competent task, OA accepts it and changes it into order and transmits the order to MA; MA issues information to SA and PA. According to the customer requirements, SA begins to design style and color, followed by printing, and die cutting. At the same time, PA starts up and transmits the task to TA. In this paper, TA and RA ensure the distribution of each task on each resource according to the inviting public bidding-bidding mechanism of CNP [7]. TA sends inviting public bidding document to RA; RA evaluates throughput itself and then bids. PA determines the equipment winning the bidding by the biding information, and sends the winning bidding information to TA and RA, and thus, the whole public bidding-bidding process can be completed (see Fig. 7). 4 Communication between agents The agent communication language (ACL) can realize the communication between agents. Knowledge query and manipulation language (KQML), which is a widely used ACL, provides a standard communication language and protocol for transmitting information and knowledge. It can be used for resolving problems and realizing information sharing. KQML consists of three layers, content layer, communication layer, and message layer. The content layer records the information content represented by corresponding language. The communication layer describes attributes parameters, including the bottom parameters and the marking of receiver and sender and connection. The message layer is the core of KQML and ensures the protocol transmitting message [8]. Three kinds of MAS, direct communication, federated communication, and centralized communication, have their own characteristics. The merit of the direct communication is that one agent cannot be influenced by other agents, which support the openness of team work. However, the disadvantage is the speed of transmitting and dealing with information will be greatly reduced with the increase of number of agents. PA is built by direct communication. The whole modeling is built by centralized communication. MA is the core of this modeling, and other agents communicate and transmit information via MA. It is easy to realize the coordination and information sharing among agents via MA. This working flow can be realized via sending information [9, 10]. The information the following: 1) Agent style, for example, MA; 2) Physics address of agent; 3) Content of information. There are three manners of linking between agents: 1) Link by to After initializing, an agent connects other agents using the address afforded by to. 2) Link by from

340 International Journal of Automation and Computing 7(3), August 2010 Fig. 7 The modeling based on MAS After receiving the request, the agent inspects the connecting purview of the agent sending the request according to the address afforded by from. 3) Link by goto Agent sends itself to the machine whose address is afforded by goto. 5 Conclusions To aim at the current production management and the process of Shenyang Songyang Paper Cup Co., Ltd., the production management modeling based on MAS was built. Comparing the modeling based on MAS and the older one, the new modeling has more superiorities. It realizes the information transmit and information sharing instantly via MA, OA, PA, and SA. All agents can work in parallel and communicate and collaborate with each other. Therefore, the time of delivery is shortened and the working flow and production efficiency are improved. All departments share the information and thus can reduce the misunderstanding. It has been found that the new modeling can improve the working flow and production efficiency and shorten the time of delivery. References [1] X. L. Qiu, H. Yi, X. Y. Wu, Z. H. Ni. Apply principalagent to actualize the agile manufacturing. Manufacturing Automation, vol. 22, no. 9, pp. 1 3, 2000. (in Chinese). [2] J. M. Usher, Y. C. Wang. Negotiation between intelligent agents for manufacturing control. In Proceedings of the 4th IEEE International Engineering Design and Automation Conference, IEEE, Orlando, Florida, USA, pp. 511 519, 2000. [3] Y. Q. Huang. Research on the Agent and Feature Technology Based Distributed CAPP and Its Application in the Manufacturing of Hydraulic Press Machine, Ph. D. dissertation, Tianjin University, PRC, 2002. (in Chinese) [4] H. T. Zhang, F. Yu, W. Li. Step-coordination algorithm of traffic control based on multi-agent system. International Journal of Automation and Computing, vol. 6, no. 3, pp. 308 313, 2009. [5] H. Yang. Multi-agent Based Agile Intelligent Manufacturing Execution System, Ph. D. dissertation, Nanjing University of Aeronautics and Astronautics, PRC, 2004. (in Chinese) [6] J. Ren, C. W. Li, D. Z. Zhao. CAN-based synchronized motion control for induction motors. International Journal of Automation and Computing, vol. 6, no. 1, pp. 55 61, 2009.

H. Li et al. / Production Management Modelling Based on MAS 341 [7] R. G. Smith. The contract net protocol: High-level communication and control in a distributed problem solver. IEEE Transactions on Computers, vol. 29, no. 12, pp. 1104 1113, 1980. [8] W. D. Zhao, H. Yi, Z. H. Ni, Y. Xing. Research on remote monitoring and control of CNC system based on Web and field bus. Journal of Southeast University (Natural Science Edition), vol. 33, no. 1, pp. 45 48, 2003. (in Chinese) [9] S. H. Lan. Research on Multi-Agent Technology and Applications, Ph. D. dissertation, Nanjing University of Science and Technology, PRC, 2002. (in Chinese) [10] Y. Q. Du, M. Z. Wang. The research of communication in multi-agent manufacturing system. Computer Simulation, vol. 20, no. 1, pp. 35 37, 2003. (in Chinese) Li He received the B. Sc. and M. Sc. degrees in mechanical engineering from Shenyang Ligong University and Northeastern University, PRC in 1993 and 2002, respectively, and the Ph. D. degree in mechanical engineering from Northeastern University in 2008. From 1993, she was a faculty member at Shenyang Ligong University. Currently, she is an associate professor in the School of Mechanical Engineering at Shenyang Ligong University. Her research interests include mechanical engineering, manufacturing information and technique, and production management. E-mail: hl0404@163.com (Corresponding author) Zheng-Hao Wang received the B. Sc. and M. Sc. degrees in mechanical engineering and engineering mechanics from Liaoning Technical University, PRC in 1983 and 1992, respectively, and from 2005, he began to study for his Ph. D. degree in mechanical engineering in Northeastern University, PRC. From 1983, he was a faculty member at Liaoning Shihua University, PRC. From 1995, he was a faculty member at Shenyang Jianzhu University, PRC. Currently, he is a professor in the School of Mechanical Engineering at Shenyang Jianzhu University, China. He is a member of Chinese Society for Vibration Engineering. His research interests include theory of vibration and rotor dynamics. E-mail: wzh200864@126.com Ke-Long Zhang received the MBA degree from Northeastern University, PRC in 2002. From 1990, he managed Shenyang Songyang Paper Cup Co., Ltd. His research interest includes production management. E-mail: syzq7600@163.com