Agent-Based Intelligent Manufacturing System for the 21
Transcription
Agent-Based Intelligent Manufacturing System for the 21
Agent-Based Intelligent Manufacturing System for the 21st Century Bing Qiao, Jianying Zhu Mechatronic Engineering Institute Nanjing University of Aeronautics and Astronautics Nanjing 210016, P. R. China Email: [email protected] Abstract: The intelligent manufacturing system is a solution to the problems of the 21st century’s manufacturing industry. In this paper, the historical evolution of manufacturing industry is outlined. According to the literature published during the last decade, 34 modern manufacturing systems and production modes and 35 mathematical methods for modern manufacturing system modeling are listed and summarized. An intelligent object called Manufacturing Agent (MA) is proposed based on the conceptual model of an agent that is being discussed and studied in Distributed Artificial Intelligence (DAI) technology, and an agent-based intelligent manufacturing system architecture is presented. Key words: Agent, Intelligent manufacturing, Multi-agent system 1. Introduction The coming century has been named as the age of knowledge economy. Manufacturing industry is facing more and more challenges. Though different technical bottlenecks for modern manufacturing have been or are being investigated, the real bottleneck is, however, the lack of knowledge (Ann 1999). Efficient acquisition and utilization of knowledge have been considered as the trump to win the competition in the knowledge economy. Fig. 1: The significance of the modern manufacturing system The base of the knowledge economy is the knowledge industry that is supported by the production equipment and modes, science and technology, management philosophy, etc., produced by the modern manufacturing system and technology. Fig.1 briefly illustrates the significance of the modern manufacturing system and its relationship to the knowledge economy. During the historical development of industry, human society has experienced three industrial revolutions. Currently, the world is undergoing a Hi-Tech industrial revolution with information technology as its main feature. As summarized in Table1, the manufacturing industry of the next century will be characterized by intensively concurrent engineering based on information technologies such as digitalization, computer network, artificial intelligence and the like. The focus of enterprise production is shifting from quantity/quality to service including quality, price, and after service. People are paying more attention to the individualization, involvement and responsive requirements of product. The management of the enterprise is being transformed from a centralized and independent model to cooperative and coordinated management, in which the creativity of people will be given full play. The 21st century’s manufacturing industry will be green. It does not damage but preserves and beautifies the environment. It not only utilizes but also saves and renews natural resources. It will play an important role in the harmonious development of humankind and nature. Table 1: Features of the three economic periods and their comparison Early industrial economy 1st industrial revolution (steam engine) 2nd industrial revolution (electricity) Large scale economy Large scale production Labor Production flowline Interchangeability Manufacturing Mechanization Unit technology Big batch Developed industrial economy 3rd industrial revolution (atomic technology) Knowledge-Based economy Hi-technology industrial revolution (information technology) Speed economy Integrated production Capital Automatic line Stiffness/Flexibility Integrated Manufacturing Automation Synthetic technology Diversification Focus of enterprise Production Management Quantity Quality Centralization Independent Relationship to natural Resource Relationship to Environment Utilization of natural resource Damages environm-ent Saving natural resource Pays attention to environment Knowledge economy Intensive production Knowledge Concurrent engineering Distributed networks Intelligent manufacturing Digitization & network Intelligent technology Individualization, involvement and rapid response Services (quality, price, after service, etc.) Cooperative, full play of personal creativity Preserve & renew natural resource Preserves & beautifies environment Industrial revolution (marked technology) Economy pattern Main production mode Main production factor Production process Production features Key technology Requirements to Products The manufacturing systems for the next century should possess the features of agility, intelligence, rapid response and favor high quality products, small batch sizes, individualization requirements, consumer involvement, and environmental consciousness (Koren et al. 1999, Lu et al. 1999, Kimura et al. 1998, Zhang et al.1997, Wiendahl and Scholtissek 1994). The manufacturing systems characterized by the above features have been named as intelligent manufacturing systems on which extensive studies have been conducted. 2. Modern Manufacturing Systems and Production Modes Over the past one or two decades, a great amount of effort has been invested by the industrial world and academia to explore the advanced manufacturing system and technology that will deal with manufacturing complexities originating from globalization of markets, stringent competition environment and overload of constraints on the manufacturing environment. And many manufacturing systems and production modes have been proposed. According to the literature published by CIRP and other manufacturing periodicals during the latest decade, nearly 34 modern manufacturing systems and production modes have been proposed. A list is given below: 1. CAD/CAM/CAE 2. GT (Group Technology) 3. TQC (Total Quality Control) 4. MRP-I (Material Requisite Planning) 5. MRP-II (Manufacturing Resource Planning) 6. FMS (Flexible Manufacturing System) 7. JIT (Just In Time) 8. CAPP (Computer Aided Process Planning) 9. DFM, DFT, … … DFX 10. CIMS (Computer Integrated Manufacturing System) 11. CDP (Customer Driven Production) 12. BR (Business Reengineering) 13. CE (Concurrent Engineering) 14. EFM (Environment Friendly Manufacturing) 15. LP (Lean Production) 16. AM (Agile Manufacturing) 17. MAMS (Multi-Agent Manufacturing System) 18. VM (Virtual Manufacturing) 19. HMS (Holonic Manufacturing System) 20. BMS (Biological Manufacturing System) 21. LCE (Life Cycle Engineering) 22. GM (Green Manufacturing) 23. CM (Collaborative Manufacturing) 24. RM (Remote Manufacturing) 25. TPM (Total Production Maintenance) 26. OAMS (Open Architecture Manufacturing System) 27. IMS (Intelligent Manufacturing System) 28. FE (Fractal Enterprise) 29. P3 IS (Pre-Planned Product Improvement System) 30. ERP (Enterprise Resource Planning) 31. SOPS (Self-Organized Production System) 32. RMS (Reconfigurable Manufacturing Systems) 33. GM (Global Manufacturing) 34. NGMS (Next Generation Manufacturing System) Some of the above manufacturing systems and production modes have been maturely applied to practical manufacturing areas and have dramatically reduced the lead time of products. Some are still undergoing investigation or conceptualization. Behind these manufacturing systems and production modes lies the continuous pursuit of design intelligence, manufacturing intelligence and management intelligence, or the enterprise intelligence as a whole. 3. Intelligent Mathematics for Modern Manufacturing System Modeling In order to model increasingly complex manufacturing systems, numerous mathematical methods, especially the so-called non-conventional mathematical tools, have been widely used. Some applications of these mathematical methods in the manufacturing arena have achieved exciting progress. Teti(1997) presented a list of intelligent computing methods for manufacturing systems and summarized their respective application aspects in design, planning, production and system level activities. Zhu reviewed 35 mathematical methods employed in manufacturing system modeling and manufacturing process controlling: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. Expert System (Knowledge Based System) Pattern Recognition Graph Theory Similarity Theory Optimization Theory Game Theory Time Series Analysis Wavelet Analysis Computer Vision Natural Language Processing Knowledge Representation Heuristic Search Constraint Based Search Confidence Theory Qualitative Reasoning Reasoning Technologies Machine Learning Machine Proving Multiple-Valued Logic Fuzzy Logic Artificial Neural Network Petri Networks Immune Networks Genetic Algorithms Artificial Life Associative Memory Blackboard Architecture Multi-Agent Systems Non-Classical Control Theory Operations Research System Engineering Simulated Annealing Combinatorial Mathematics 34. Fractal Theory 35. Chaos Theory In the practical applications, these methods are usually combined with each other to deal with planning, design, process control, and system integration, etc. Research examples can be frequently found in technical literature. The wide application of these intelligent mathematical methods or their combinations in manufacturing will definitely promote the development of manufacturing system modeling and provide new solutions to the complexities manufacturing. 4. Agent and Agent-Based Manufacturing Currently, the open architecture Multi-Agent System (MAS) has become the main research direction of DAI. Agents are now being widely discussed by researchers in mainstream computer science (Wooldridge and Jennings 1995). Though there is not a universally accepted definition of agent, it does not prevent people from expanding the application space of agent technology. In practical application an agent is usually considered as a self-directed program object which has its own value system and the means to solve certain subtasks independently and then communicate its solution to a greater problem solving process, either on its own initiative or on request from other agents (Sikora and Shaw 1997, Wiendahl and Ahrens 1997). Fig.2 is a conceptual model of an agent, which comprises 4 components: • • • • • Knowledge processor, a knowledge base system that stores and processes the necessary knowledge for an agent to play the role the agent society has designed for it. Perception, a channel for an agent to receive information from the external world. Effector, an interface for an agent to modify or influence the state of the agent community. Communication, a mechanism for an agent to exchange views with other members in the agent society. Objectives, a list of roles for an agent to play. Fig. 2: Conceptual structure of an agent The MAS is an open and distributed system that is formed by a group of agents combined with each other through a network for cooperatively solving a common problem. The MAS has been applied in many areas. The modern manufacturing system, which is a highly decentralized system, is one of the typical representatives of MAS application. A great deal of research effort has been devoted to agent-based manufacturing. Some leading manufacturers and government agencies in the USA are claiming that agent-based manufacturing is the future for US manufacturing and agent technology is the fundamental technology for implementing the agile manufacturing vision (Baker 1998). There is multifold advantage in using an agent-based approach for manufacturing. First, manufacturing information is stored and processed in a distributed manner as opposed to being stored in one large program. Second, it makes the incremental improvement of the manufacturing system possible through the learning and cooperation in agents. Third, it provides a promising method for enterprise integration. 5. MA and Agent-Based Intelligent Manufacturing System Architecture The application of agent technology in many manufacturing aspects have been investigated, however, a general agent model suitable for manufacturing applications still remains to be constructed. 5.1 Intelligent Manufacturing Agent In manufacturing, an agent represents an object with certain intelligence, which is either a physical object such as a worker, a machine tool, and the like or a logical object such as an order, a task, etc. In order to establish an agent-based manufacturing system, the model of the Manufacturing Agent (MA) must first be defined. Fig.3 is the conceptual model of MA. Procedure repository, inference mechanism, knowledge base, and perception processor constitute the knowledge processing unit for the MA. The self-learning, self-organizing, and self-adapting are the optimizers of the MA. The simulation expert provides behavior evaluations for the MA. Working memory stores the temporary data produced by the other parts of the MA. The communication mechanism manages the message exchanging tasks of the MA with the other members of the system. The coordinator coordinates the internal functions of the MA, meanwhile it receives the coordination requests from other MAs. Fig. 3: Model of MA The main features of MA are: • • • • • Self similarity: Every MA has a similar structure. Self learning: The MA has coordinated learning ability that contributes to an emergent improvement of the whole system behavior. Self adaptation: The MA has the ability to adjust its behavior intention according to different rules. Self organization: The MA can organize or configure its internal procedures to deal with different tasks. Self maintenance: The MA can monitor the state of itself and keep itself in the most efficient condition. 5.2 Agent-Base Intelligent Manufacturing System Architecture There are three commonly studied multi-agent systems: functional, blackboard, and heterarchical architectures. The heterarchical multi-agent architecture shows a greater promise in the application of manufacturing for its self-configuration, scalability, fault tolerance, reduced complexity, increased flexibility, reduced costs, massive parallelism, Internet compatibility, and virtual enterprise formation (Baker 1998). In a heterarchical multi-agent manufacturing system, MAs communicate as peers and there are no fixed master/slave relationships. The objectives of the system are achieved through cooperation of MAs. According to the general structure of the manufacturing enterprises, we propose the agent-based manufacturing system architecture depicted by Fig.4. Fig. 4: Agent-Based Architecture for Manufacturing Enterprise In Fig.4, the whole operation logic of a manufacturing enterprise is divided into 4 parts: central part, management, planning, and production. Each part consists of a group of MAs. The central MAs are designed for the leadership of the enterprise to coordinate and control the operation of the whole enterprise. The management MAs execute the management functions of the enterprise. The planning MAs arrange and allocate the available and potential resources to support the profit seeking objectives of the enterprise. And the production MAs conduct shop floor control and management of the manufacturing. All these MAs are connected to the distributed enterprise network or Intranet. This MA network is heterarchical in nature. Each MA has its own behavior objectives and local knowledge about the manufacturing process. In this agent-based manufacturing system, humans communicate with MAs through a special MA, for example a Personal Assistant (PA) ( Pan and Tenenbaum 1991). 6. Concluding Remarks This paper has outlined the historical evolution of manufacturing and enumerated the main features of 21st century manufacturing. 34 modern manufacturing systems and production modes and 35 mathematical methods for modern manufacturing system modeling are listed and summarized. An intelligent object called Manufacturing Agent (MA) is proposed based on the conceptual model of the software agent, and an agentbased intelligent manufacturing system architecture is presented. In a multi-agent system, the objective of the system is achieved through the cooperation of agents. Each agent has only local knowledge about the whole agent environment and has only local behavior ability, however, the cooperative and coordinated interaction of multiple agents usually results in a macro efficient system behavior that is robust, flexible and error-tolerant. This kind of emergent intelligent behavior can be found in many biological systems such as the ant colony, bird flocks, fish schools, and wolf packs (Baker 1998). What makes agent-based manufacturing attractive is just this emergent behavior intelligence. To successfully employ agent-based technology in manufacturing, some fundamental problems should be researched. They include the coordination mechanism of MAS, the communication protocol of agent, behavior intelligence of agent, human involvement in agent system, the formalized modeling of agent-base manufacturing, etc. It is completely believable that the agent will have an impact on future manufacturing. 7. Acknowledgement This research is supported by the Chinese National Science Foundation under grant contract no. 59990470. 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