Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System

Transcription

1 International Journal of Automation and Computing 7(4), November 2010, DOI: /s Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System Zhong-Qi Sheng 1 Chang-Ping Tang 1 Ci-Xing Lv 2 1 School of Mechanical Engineering and Automation, Northeastern University, Shenyang , PRC 2 Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang , PRC Abstract: Agile intelligent manufacturing is one of the new manufacturing paradigms that adapt to the fierce globalizing market competition and meet the survival needs of the enterprises, in which the management and control of the production system have surpassed the scope of individual enterprise and embodied some new features including complexity, dynamicity, distributivity, and compatibility. The agile intelligent manufacturing paradigm calls for a production scheduling system that can support the cooperation among various production sectors, the distribution of various resources to achieve rational organization, scheduling and management of production activities. This paper uses multi-agents technology to build an agile intelligent manufacturing-oriented production scheduling system. Using the hybrid modeling method, the resources and functions of production system are encapsulated, and the agent-based production system model is established. A production scheduling-oriented multi-agents architecture is constructed and a multi-agents reference model is given in this paper. Keywords: Agile manufacturing, intelligent manufacturing, production scheduling, system modeling, agent technology. 1 Introduction With the advent of market globalization and the acceleration of technology advancement, the manufacturing environment has undergone profound changes and the manufacturing industry is facing new opportunities and challenges. In order to meet the increasingly fierce market competition and the development needs of the enterprises, various manufacturing paradigms have come into being [1 3]. As the representative one, the agile intelligent manufacturing paradigm emphasizes collaboration among the enterprises at manufacturing execution level in order to fully utilize the manufacturing resources of different enterprises to achieve agile response to market changes. In the agile intelligent manufacturing paradigm, the management and control of production systems has surpassed the scope of individual enterprise, and shows some new features including complexity, dynamicity, distributivity and compatibility, which make the management and control of manufacturing systems more complex. Both the control mode together with organization architecture and the decision-making criteria have undergone profound changes. This manufacturing paradigm calls for the production scheduling system that could support the cooperation among various production sectors, the sharing and distribution of various resources so as to achieve the rational organization, scheduling and management of production activities, process the uncertainties and unexpected events in distributed production environments, and obtain the ability of quickly adapting to market change and technology advancement. In the agile intelligent manufacturing paradigm, the production scheduling system has its own characteristics and needs. The creation of a dynamic, open, flexible, scalable production scheduling system model becomes the primary task for constructing the production system. Manuscript received January 1, 2010; revised March 12, 2010 This work was supported by Fundamental Research Funds for the Central Universities (No. N ). Building a new architecture of the production scheduling system adapting to the agile intelligent manufacturing paradigm, establish a strong production and operation mechanism to achieve the unified management of manufacturing resources in various enterprises, and realize production goals are the immediate needs for the production scheduling system. This paper develops a production scheduling system for the agile intelligent manufacturing paradigm based on distributed multi-agents technology. According to the requirements of the agile intelligent manufacturing system on production scheduling, this paper puts forward a hybrid modeling method to encapsulate the resources and functions of the production system together. Also, it establishes the production system model based on agents, and constructs the production scheduling systemoriented multi-agents architecture and reference model. 2 Production scheduling system in agile intelligent manufacturing paradigm 2.1 Production system in agile intelligent manufacturing paradigm Agile intelligent manufacturing conforms to the development trend of the modern manufacturing system in the new century and provides an effective means for manufacturing enterprises to adapt to the globalizing market competition and strengthen their core competitive force. The basic organizational form of agile intelligent manufacturing is dynamic alliance. In order to promptly respond to manufacturing tasks, the core enterprise chooses different manufacturing cells in itself and its allies, and organizes the manufacturing resources together logically to form a virtual manufacturing system that has actual manufacturing capabilities. This system is able to perform the scheduling, management and control of resources as a common intel-

2 Z. Q. Sheng et al. / Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System 597 ligent manufacturing system that has virtual form and at the same time has certain intelligent features. The production system in the agile intelligent manufacturing paradigm is reconfigurable along with the rapid changes of the environment and has the following characteristics. 1) Distributed coordination Manufacturing equipment come from different companies, factories, and workshops. They are distributed in different locations and form a collection of manufacturing resources by logical relationships. 2) Autonomy In production management, manufacturing system has the autonomy, and through collaboration of manufacturing resources, the system can respond effectively to the changes of product variety, product upgrading, as well as other unusual events to achieve self-regulation. 3) Dynamic A logical manufacturing system is made up of dynamic manufacturing resources at different positions according to market demands and manufacturing tasks. The control of these resources is returned to their original environment when tasks are completed. This new manufacturing system faces more dynamics than the traditional manufacturing system. 2.2 Production scheduling in the agile intelligent manufacturing paradigm Production scheduling allocates the time and resources to production activities, and optimizes production planning according to specific rules and constraints. A typical production scheduling problem generally includes a set of tasks, a collection of resources, a set of constraints and a set of performance indicators. The goal of the production scheduling is to rationally allocate resources for all jobs, arrange the processing order of each job to satisfy the constraints while optimizing some or all of the production performances. The agile intelligent manufacturing system is a kind of manufacturing system that is constructed to adapt to different manufacturing targets, which can reconfigure dynamically along with the changes of the environment. Using the manufacturing resources in different enterprises and through the rapid reconfiguration of manufacturing resources, the manufacturing system turns the product from design process to production process quickly at reasonable cost, which can adapt to the ever-changing market and personalized needs of users. With the support of a network, the enterprises have increased cooperation during the manufacturing process, especially in the manufacturing execution level, and emphasize on the connection and integration of information for process equipment to maximize the capacity of process equipment. Production scheduling in the agile intelligent manufacturing paradigm means that with the support of information and network technology, manufacturing resources could be shared and allocated in various sectors of manufacturing activities. The rational organization, scheduling and management for production activities can be achieved so as to complete the manufacturing tasks efficiently and respond to market opportunities in time. Production scheduling in the agile intelligent manufacturing paradigm has similar functions with conventional production scheduling. However, because the manufacturing process will be jointly undertaken and completed by a number of distributed manufacturing resources, the production scheduling emphasizes more on the autonomy of manufacturing resources and the interoperability among the resources. The features of production scheduling in agile intelligent manufacturing paradigm include: 1) The schedulers no longer have full control over manufacturing resources, and are not able to master full production operation process and obtain all historical information, which leads to uncertainty and creates the greatest difficulty in scheduling. 2) Production planning and scheduling has the autonomy. Resources are relatively independent and have an independent power of decision-making. While meeting the premise of common interests, the maximization of local interests is pursued. There is no mandatory centralized control among resources. Both competition and cooperation exist, so collaborative mechanism is needed to coordinate the decisions of production planning and scheduling. 3) Resources and tasks in production systems have a dynamic nature. Manufacturing resources at different locations make up a logical manufacturing system and the control of these resources is returned to their original environment when the tasks are completed. This new type of manufacturing system faces more dynamics than traditional manufacturing systems. 4) Production planning and scheduling requires synchronization. Production scheduling needs to take advantage of real-time data to make real-time planning to quickly deal with emergencies such as delays in the supply of materials, failure of production equipment, emergency insertion of orders, and so on. 2.3 Agile intelligent manufacturingoriented production scheduling system The agile intelligent manufacturing paradigm puts forward new demands to the production scheduling system. The production scheduling system is required to have more rapid response capability and self-organizing, self-adaptive and collaborative features. In addition to the characteristics such as stability, reliability, and maintainability that classical scheduling systems should have, new features including coordination, agility, reconfigurability, scalability, and adaptability are also needed as follows: 1) Adaptability When the production environment is changed, the production scheduling system can adjust its scheduling policy and logic to adapt to this change. 2) Openness The architecture of cell control system can easily accept new features and functions. Its organizational architecture can easily realize the inheritance of various manufacturing resources and permit manufacturing resources to join and exit online. The production scheduling system can easily add new functional modules. 3) Flexibility The organizational architecture of production scheduling system should have the flexibility to schedule various man-

3 598 International Journal of Automation and Computing 7(4), November 2010 ufacturing resources to complete different processing tasks to achieve various scheduling logic. 4) Configurability, scalability, and fault-tolerance Production scheduling system can adjust scheduling policy quickly according to market requirements and environment condition, and also can adapt to different production systems to combine the modules and add new function modules. The exception of the partial production scheduling system does not lead to the collapse of the entire system. These requirements put forward higher demands on the modeling of the production scheduling system. 3 Production scheduling system modeling based on agent An agent is a kind of highly autonomous entity with independence, interactivity, and reactivity, which can complete its work independently and accomplish the target of the whole system through communication and coordination. The system developed with agent technology has the characteristics including distribution, openness, and intelligence, and can achieve global optimization on the basis of partial autonomy. A production scheduling system constructed on the basis of agent technology can satisfy the requirements of the production system in agile intelligent manufacturing paradigm. On one hand, solving strategies of distributed multi-agents can meet the production scheduling needs. On the other hand, the interactivity, collaboration, and autonomy of agents ensure that the system is open, scalable, reusable, and so on. 3.1 Modeling principles of production system based on agent The production system in the agile intelligent manufacturing paradigm contains a variety of manufacturing resources and entities, and includes complex logistics flow and information flow. When establishing the agent-based model of the production scheduling system, various factors and their mutual relations must be considered. Agent system modeling is closely related to the solving of a problem. Considering the characteristics and needs of production scheduling in the agile intelligent manufacturing paradigm, the modeling principles are determined as follows: 1) Taking into account manufacturing processes, management functions and production resources, a mixed modeling approach is used, which includes resources agent model and process agent model together with function agent model. 2) Agent entity should have a relatively concentrated local knowledge base and the knowledge overlap with other entities should be little. 3) It is attempted to reduce the type and extent of direct or indirect information interaction among agents. 4) The independence and distribution of agents is emphasized, which means that agent entities should focus on their work at most times, and will only interact when necessary. 3.2 Agent identification method in production scheduling system To establish an agent-based system, first the constituent elements of the object system are described and packaged using agents, thus they are managed and controlled by the agents. This process is known as agent identification [4]. To build an agent-based production scheduling system, first the actual manufacturing system is decomposed into some components and these components are packaged into agents, then the manufacturing system is modeled as an agent system. Existing modeling methods can be divided into three categories: function-based modeling method, physical entity-based modeling method, and problem-based modeling method. In physical entity-based modeling method, there are clear correlations between agents and physical entities [5]. The agents obtained through physical decomposition independently define and efficiently manage a group of states, which can reduce the amount of interactive communication. In function-based modeling method, there are no clear correlations between agents and physical entities [6]. The agents obtained through functional decomposition must share a lot of state variables, which leads to consistency problems and some unnecessary interactions. Although functional decomposition method in system realization is more difficult than physical decomposition method, it is useful to deal with the system-level problems including the integration of existing systems, the issues of legacy systems and inter-enterprise integrations of heterogeneous systems. Therefore, when mapping the production system into the agent system, physical decomposition should mainly be used, and the functional decomposition approach is adopted to provide system-level services. In this paper, hybrid modeling method is employed. Physical entity-based modeling method and function-based modeling method are used at the same time to model the production system. There are three types of agents used in the system. The first is obtained by packaging the basic elements of the manufacturing system such as resource agents, task agents, etc. The second is introduced to improve the system performance including resource mediator agents, task mediator agents, collaborative support agents, and mobile tender agents, etc. The last is used to manage and maintain the system, for example, system management agents, directory service agents, etc. These agents connect together by network as shown in Fig. 1, through autonomous decision-making and coordination together with cooperation among them to achieve the production scheduling function and the goals of the system. In Fig. 1, TA denotes task agent, RA denotes resource agent, MA denotes mediator agent, and YA denotes yellow page agent. Fig. 1 Agent-based production scheduling system

4 Z. Q. Sheng et al. / Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System Organization architecture of agentbased production scheduling system After mapping the physical elements, functional elements and logical elements of the production scheduling system into agents, a proper system architecture is required to organize a large number of agents. The organization mode of the agent system determines the system behaviors, and also decides the ways of mutual collaboration and the architecture of problem solving. The organizational architecture of agents describes the organization mode of the agent system that is formed by mapping the functional entities into agents including static and dynamic characteristics. 4.1 Manufacturing system-oriented agent organization architecture Many scholars have proposed different agent organization architectures in accordance with the production control architectures, which can be divided into three categories: hierarchical architecture, federal architecture, and full self-government architecture. Because the agent-based manufacturing system contains many agents, among which the communication is large and the collaborative process is complex, this paper combines the federal architecture and the hierarchical architecture together and constructs a hybrid agent architecture. This hybrid architecture provides an open and extensible framework, which ensures the stability and scalability of the system. Agents at different levels of the system are responsible for different tasks, which simplifies the computational complexity of the system and increases the system control ability. In the system, there exist two kinds of agents, static agents, and dynamic agents. Static agents are the agents present all along from system initialization to system operation, which is usually used to provide some services such as yellow page agents, mediation or other auxiliary agents. Dynamic agents are immediately created agents according to system operation needs, such as resource agents created according to manufacturing resource configuration, agents created by production tasks and mobile agents created during the auction bidding, etc. The hierarchical form that combines static agents and dynamic agents together is particularly suitable to develop a complex, dynamic production scheduling system composed of a lot of resource agents and task agents. In this hybrid architecture, cooperative support agents are introduced, which choose agents that will participate in collaboration, cooperative agreements and collaboration beginning and end conditions according to the context of collaboration (determined mainly by production targets, production environment, and agents working environment). Collaborative context can be perceived by cooperative support agents, and also will be informed to cooperative support agents after being perceived by other agents. For example, while mechanical failure appears, the original collaboration agreement will no longer be feasible, at this time the agent corresponding to the failed machine will notify this condition to cooperative support agents. Cooperative support agents will terminate the ongoing collaboration and start a new collaboration to complete the scheduling of new environment. The introduction of cooperative support agents improves system adaptability to production goals and the environment. Through the introduction of mediator agents, a group of agents could be aggregated as an agent collection, in which the agents can communicate and coordinate their behaviors through a coordinator. At the same time, representing the whole agent collection, the mediator agent communicates and coordinates the behaviors with other mediator agents or agents in the system, which can simplify the complexity of communication and control. Mediator agents have a higher global view and coordination capacity, emphasize more on the intelligence of agents, and also can play a certain role in some collaborative processes. For example, the resource mediator agents serve as the auctioneer s roles in combinatorial auction and are responsible for launching auction, clearing market and adjusting price. Task agents and resource agents are in the bottom level of the system, and mainly focus on fast response and real-time control. The high-level agents only provide the objectives and constraints to the low-level agents and do not control the activities of the low-level agents. The positions of agents among the same level are equal. The agent-based hybrid architecture integrates the advantages of distributed architecture and hierarchical architecture, while allowing horizontal consultation and vertical consultation. The collaboration information among the tasks can be distributed to the heterogeneous environment. The functions and targets can be achieved by autonomous decision-making and coordination of agents. 4.2 The features of production schedulingoriented agent system architecture The main goal of the hybrid architecture proposed in this paper is to build a flexible architecture that can change according to the manufacturing environment and needs dynamically and at the same time ensure the entire system to keep a certain optimization capacity and anti-disturbance capacity. The hybrid control architecture matches well with the logical structure of the actual system, and can ensure that the entire scheduling system has the flexibility and adaptability to dynamic changes, and is predictable near optimal scheduling results. The hybrid control architecture also has open, reconfigurable, agile, and dynamic structural characteristics, which can be able to meet the production scheduling needs in the agile intelligent manufacturing paradigm, whose main features are described as follows: 1) Dynamic collaboration architecture based on collaboration support agents The different contexts of collaboration can be reflected in different production environments. Collaboration support agents determine the collaboration agents in participation, collaborative model, and collaboration strategy. 2) Federal architecture based on resource mediator agents and task mediator agents The intermediary service method of coordinator can reduce the cost of the coordination activities among the agents, and also can represent the resource owners to auction resources. The resource agents in this architecture do not need to know about the detailed information of other agents, which reduces the communication complexity and control complexity of the distributed system to ensure the

5 600 International Journal of Automation and Computing 7(4), November 2010 stability and scalability of the system. 3) Combination of general agents and mobile agents In some collaborative agreements, mobile agents are used to represent the task agents to take part in the resources bid, which can reduce the network traffic effectively. 4) Openness The number and relationships of agents in the system are not limited by the architecture itself, while being determined by the different size and need of the system. 5) Dynamicity The lifecycle and distribution in the system of each agent can change dynamically. In accordance with the actual circumstances and needs of the system, the system can dynamically generate, modify, combine, and delete the agents. 6) Modularity The entire architecture does not only correspond to the structure and functions of the actual production scheduling system, but is also very simple and easy to implement. Each agent component has intelligence and autonomy with local knowledge and information so that the whole system has strong flexibility, agility, and fault-tolerance. 5 Agent architecture reference model of production scheduling system After identifying the agents of the system and establishing the organization structure of the system, it is needed to further define the composition modules of the agents, as well as the relationship among these modules to establish a unified structure of a reference model for all types of agents. 5.1 Structure model of the agent The structure model of the agent shows the composition modules of the agent and the relationships among the modules. Scholars and researchers have designed many different agent structures based on the system need, which can be divided into three types: deliberative agent structure, reactive agent structure, and mixed agent structure. People usually call the agents with these structures as deliberative agent, reactive agent, and mixed agent [7 10]. In the agent-based production scheduling system, the basic functions of the agents include two aspects: one is planning local activities and adopting effective strategies in order to accomplish their own goals, and the other is carrying out effective collaboration in order to accomplish the overall objectives of the system, in which the collaborative approach could change along with the change of the state and the goals of production scheduling system. Based on the mixed-type agents, considering adaptive collaboration needs among agents, this paper establishes the unified composite agent structure suitable for the production scheduling system of agile intelligent manufacturing as shown in Fig. 2. This includes perception, modeling, decision-making and management, implementation and other components as well as knowledge base. 1) Perception component It perceives the external environment and makes certain abstraction on relevant information. According to the information type, perception components send the abstracted information to the modeling components. 2) Modeling component It contains the cognitive model established by the agent toward the world and other agents. Based on these cognitive models and the current perceived situation, the agent can predict the short-term situation and then formulate the corresponding decision-making. Some common knowledge is stored and forms agent conviction. 3) Decision-making and reasoning component It corresponds to the brain of the agent and mainly completes the decision-making and the behavior management, which includes local decision-making, collaboration control, behavior generation, and implementation planning. 4) Implementation component According to the intent of the agent formed from decision-making component, it carries out the corresponding actions and exerts influence on the environment. 5) Knowledge base It stores the necessary knowledge when the agent conducts decision-making, which includes the descriptive knowledge base, the production scheduling-related descriptive knowledge such as the models of its own and the other agents and the environment models, and rule base, which include the conduct rules, expert control tables, scheduling strategy needed while the agents run and collaborate. 6) Database It stores the attributes, abilities, status, and other data of the agents, and provides the necessary data sources for the agent s own decision-making and cooperation among the agents. Some information would dynamically change along with the operation of the system and the interaction among the agents, which includes static data, dynamic data, result data, tasks list, and so on. Fig. 2 (a) Detailed structure of agent (b) Logic structure of agent Reference structure of agent In order to improve the ability of the system in adaptability and reconfiguration and to reduce the complexity of system design, implementation, and maintenance, these function components are designed using the modular structure. In this way, all agents have the same basic structure, but the specific content in each module is different. When

6 Z. Q. Sheng et al. / Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System 601 the agent works, a subset of these function modules may be only used. Based on the structure model of the agent introduced above, the agent can be defined as follows in the form of multi-element group: Agent= AID, STATUS, TRG, BHV, MESS, SENS, ACT in which AID is the identifier of the agent that is used to identify the agent; STATUS is the status set of the agent; TRG is the set of the agent s triggering status, which indicates the status that the agent can (or need) identify; BHV is the set of the agent s behavior; MESS is the message collection of the agent received and sent. MESS = MESSIN, MESSOUT, in which MESSIN indicates the set of received messages from outside and MESSOUT indicates the set of the messages sent by the agent to the external; SENS is perception function, through which the agent identifies the external sources and accesses to the trigger state in accordance with the internal condition; ACT is activities function, which indicates the action taken by the agent in current trigger state. Based on the definitions above, the behavior ability of an agent can be simply summarized as the perceived ability, the ability to control themselves and the ability of mobility, in which the role of the agent in the process of problemsolving can be summarized as the explanation, classification, conversion, control, and so on. 5.2 The description of various agents Considering the functions and logical relations of various elements in the scheduling system, according to the requirements of production scheduling system in the agile intelligent manufacturing paradigm and the identification method of agents presented in this paper, eight kinds of agents can be obtained, through the autonomous decisionmaking and cooperation of which the production scheduling can be carried out. The entity chart of the agent-based production scheduling system is shown in Fig. 3. Eight kinds of agents are named as follows: resource agent, resources mediator agent, task agent, task mediator agent, mobile submit agent, the coordinate mediator agent, yellow page agent and management agent. Fig. 3 Types of agents in the system 1) Task mediator agent As a kind of intermediary agent, task mediator agent is the manager of the order task and the coordinator/supervisor of task implementation. It receives production tasks from the ERP/MRPII systems of the enterprise or other collaborative partners, which releases manufacturing collaboration request, and monitors and coordinates operation activities. When the production tasks come into the system, the task mediator agent creates a task agent based on decomposed sub-task. The task agent contains the system objectives, resource constraints, production process, process requirements, as well as the priority of the task and other information. 2) Task agent Dynamically generated by the task mediator agent, the task agent is created along with the processing task and withdrawn when the processing task is completed. They make the different forms of production tasks in their product life cycle (such as raw materials, semi-finished or finished) unified and are the links contacting various equipment needed in the production process. Task agent is the interim manager of the work-piece task. Its function is to generate the task list of the manufacturing process and the operation demand table based on task objectives and requirements. The task agent selects appropriate manufacturing resources, ensures a viable production plan and coordinates with related resources agent to meet the performance targets such as delivery date to complete the workpiece task. 3) Resource mediator agent As a kind of intermediary service agent, the functions of the resource mediator agent include providing registration and information intermediary services for associated manufacturing resources and monitoring the status of various resources. When a manufacturing resource such as a machining equipment comes into the system, the resource mediator agent generates a corresponding resource agent and registers its information such as location and function information. When the location, function or other information of the device changes, the resource agent will notify the resource mediator agent in time. When the device leaves the system, the device s agent first sends cancelation information to the resources mediator agent and deletes all information. After the resources mediator agent confirms, the device s agent will write themselves off. In this way, the resources mediator agent can master all manufacturing resources information of the system, communicate directly with all manufacturing resource agents, and constantly updates the resource database. 4) Resource agent (RA) The resource agent is the package of all kinds of resource objects in the production system, which may be general machine tools, computer numerical control (CNC) machine tools, machining centers, or flexible production lines. The resource agent is responsible for the collection and preservation of manufacturing resources information such as device name, type, processing capacity, cost, processing condition, maintenance plans, and other information. The resource agent is both the manager of production resources and the executive of production tasks. Its main function is to access production tasks in accordance with its ability and to select the strategy according to all requirements of technology and precision to complete optimal scheduling of production tasks of the equipment. 5) Coordinate mediator agent

7 602 International Journal of Automation and Computing 7(4), November 2010 In order to improve the collaboration efficiency and adaptability of the agent, the coordinate mediator agent is introduced. According to the collaboration context determined by production targets, tasks, environment and working environment of the agent, coordinate mediator agent selects a suitable collaborative model, launches collaboration among agents, and monitors the collaborative process. 6) Mobile submit agent In the collaboration model based on combinatorial auction, in order to get satisfactory auction result, many interactions will happen between resource mediator agent and task agent. In order to reduce communication cost after the start of the collaboration agreement, the task agent creates a mobile submit agent in accordance with its goals and beliefs. Carrying related code and knowledge of the task agent, the mobile submit agent moves to its destination, the resource mediator agent tenders on behalf of the task agent and brings the results back after the auction. 7) Yellow page agent Yellow page agent is also known as directory service agent, which provides yellow pages services including registration, search, locating and write-off. The agent may register its ability or service information to yellow page agent, and inquire information from yellow page agent in order to find other agents that can provide services for achieving its goals. 8) System management agent The system management agent is primarily used to provide naming services, which is to ensure that each agent has a unique name, and management capabilities of the platform, and monitor the agent s visit to the platform. 6 Conclusion By analyzing the features and requirements of the production scheduling system in the agile intelligent manufacturing paradigm, this paper studied the modeling principles of the system and the identification method of the agents in the agent-based production scheduling system. Also, it provided a hybrid organization structure oriented on the multi-agent production scheduling system, and presented the structure model of agents as well as the definition and functions of various agents. 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H. Norrie. Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, vol. 20, no. 4, pp , [7] S. De Vries, R. V. Vohra. Combinatorial auctions: A survey. INFORMS Journal of Computing, vol. 15, no. 3, pp , [8] H. P. Shang, Z. X. Zhao. Integration of manufacturing services into virtual environments over the Internet. International Journal of Automation and Computing, vol. 1, no. 1, pp , [9] F. T. S. Chan, H. K. Chan, H. C. W. Lau, R. W. L. Ip. Analysis of dynamic dispatching rules for a flexible manufacturing system. Journal of Materials Processing Technology, vol. 138, no. 1 3, pp , [10] 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 , Zhong-Qi Sheng received the B. Eng. and M. Eng. degrees in mechanical engineering from Northeastern University, PRC in 1994 and 1997, respectively, and the Ph. D. degree in mechanical manufacturing from Northeastern University in In 1997, he was a faculty member in Northeastern University. Currently, he is an associate professor in School of Mechanical Engineering and Automation at Northeastern University. He received the Best Paper Award of the International Conference on Information Management, Innovation Management and Industrial Engineering in His research interests include digital manufacturing, enterprise integration, and product data management. zhqsheng@mail.neu.edu.cn (Corresponding author) Chang-Ping Tang received the B. Eng. degree in mechanical manufacturing and automation from Shenyang University of Technology, PRC in Currently, he is a graduate student at School of Mechanical Engineering and Automation in Northeastern University, PRC. His research interests include mechanical manufacturing and automation. tang changping@163.com Ci-Xing Lv received the B. Eng. and M. Eng. degrees in mechanical engineering from Jilin University, PRC in 1999 and 2002, respectively, and the Ph. D. degree in mechatronics engineering from Shenyang Institute of Automation, Chinese Academy of Sciences, PRC in Currently, he is an associate professor in Department of Industrial Informatics at Shenyang Institute of Automation. His research interests include logistics, supply chain management, and artificial intelligence. smale@sia.cn