巷道式自動化立體車庫升降部分

巷道式自動化立體車庫升降部分,巷道,自動化,立體車庫,升降,部分 附錄1：翻譯（英文） Modeling and specifcations of dynamic agents in fractal manufacturing systems Kwangyeol Ryua, Youngjun Sonb, Mooyoung Junga,* a Department of Industrial Engineering, Pohang University of Science and Technology, Pohang, South Korea Systems and Industrial Engineering Department, The University of Arizona, Tucson, AZ, USA b Received 9 September 2002; accepted 16 April 2003 Abstract In order to respond to a rapidly changing manufacturing environment and market, manufacturing systems must be flexible, adaptable, and reusable. The fractal manufacturing system (FrMS) is one of the new manufacturing paradigms that address the need for these characteristics. The FrMS is comprised of a number of ‘‘basic components’’, each of which consists of five functional modules: (1) an observer, (2) an analyzer, (3) an organizer, (4) a resolver, and (5) a reporter. Each of these modules, using agent technology, autonomously cooperates and negotiates with others while processing its own jobs. The resulting architecture has a high degree of self-similarity, one of the main characteristics of a fractal. Despite the many conceptual advantages of the FrMS, it has not been successfully elaborated and implemented to date because of the difficulties involved in doing so. In this paper, the static functions and dynamic activities of each agent are modeled using the unified modeling language (UML). Then, relationships among agents, working mechanisms of each agent, and several fractal-specific characteristics (selfsimilarity, self-organization, and goal-orientation) are modeled using the UML. Then, a method for dealing with several types of information such as products, orders, and resources in the FrMS is presented. Finally, a preliminary prototype for the FrMS using AgletsTM is presented. # 2003 Elsevier B.V. All rights reserved. Keywords: Fractal manufacturing system (FrMS); Agent technology; UML; Modeling Abbreviations: FrMS, fractal manufacturing system; BFU, basic fractal unit; DRP, dynamic restructuring process; UML, uni?ed modeling language; HMS, holonic manufacturing system; BMS, bionic/biological manufacturing system; CNP, contract net protocol; MANPro, mobile agent-based negotiation process; NMA, network monitoring agent; EMA, equipment monitoring agent; SEA, schedule evaluation agent; DRA, dispatching-rule rating agent; RSA, real-time simulation agent; SGA, schedule generation agent; GFA, goal formation agent; TGA, task governing agent; NEA, negotiation agent; KDA, knowledge database agent; DMA, decision-making agent; FSM, fractal status manager; FAM, fractal address manager; REA, restructuring agent; NCA, network command agent; ECA, equipment command agent; STA, system agent; NTA, network agent; MP, material processor; MH, material handler; MT, material transporter; BS, buffer storage; MRP, material removal processor; MFP, material forming processor; MIP, material inspection processor; PD, passive device; FMH, ?xed material handler; MMH, movable material handler; FMT, fixed material transporter; MMT, movable material transporter; ABS, active buffer storage; PBS, passive buffer storage E-mail address: myjung@postech.ac.kr (M. Jung). * Corresponding author. Tel.: t82-54-279-2191; fax: t82-54-279-5998. 0166-3615/$ – see front matter # 2003 Elsevier B.V. All rights reserved. doi:10.1016/S0166-3615(03)00099-X 1. Introduction Facing intensified competition in a growing global market, manufacturing enterprises have been reengineering their production systems to achieve computer integrated manufacturing (CIM). Major goals of CIM include, but are not necessarily limited to, lowering manufacturing costs, rapidly responding to changing customer demands, shortening lead times, and increasing the quality of products [1–3]. However, the development of a CIM system is an incredibly complex activity, and the evolution to CIM has been slower than expected [4,5]. This can be directly attributed to high software development and maintenance costs. Therefore, in order to achieve a competitive advantage in the turbulent global market, the manufacturing enterprise must change manufacturing processes from all angles including ordering, product design, process planning, production, sales, etc. As a control model for implementing CIM systems, hierarchical decomposition of shop floor activities has been commonly used in the shop floor control system (SFCS), the central part of a CIM system [2]. Generally, a central database provides a global view of the overall system, and controllers generate schedules and execute them. Hierarchical control is easy to understand and is less redundant than other distributed control architectures such as heterarchical control. However, it has a crucial weak point, which is that a small change in one level may significantly and adversely affect the other levels in the hierarchy. Therefore, it is normally said that hierarchical control of CIM systems is much more suitable for production in a steady environment than in a dynamically changing environment because it is so diffcult to apply control hierarchy changes immediately to the equipment. Furthermore, it is diffcult to meet dynamically changing customer requirements because the hierarchical control architecture is not flexible enough to handle the reconfiguration of the shop. Therefore, the manufacturing system of tomorrow should be flexible, highly reconfigurable, and easily adaptable to the dynamic environment. Furthermore, it should be an intelligent, autonomous, and distributed system composed of independent functional modules. To cope with these requirements, new manufacturing paradigms such as a bionic/biological manufacturing system (BMS) [6,7], a holonic manufacturing system (HMS) [8,9], and a fractal manufacturing system (FrMS) [10–13] have been proposed. Tharumarajahet al. [14] provide a comprehensive comparison among a BMS, a HMS, and an FrMS in terms of design and operational features. An FrMS is a new manufacturing concept derived from the fractal factory introduced by Warnecke [13]. It is based on the concept of autonomously cooperating multi-agents referred to as fractals. The basic component of the FrMS, referred to as a basic fractal unit (BFU), consists of five functional modules including an observer, an analyzer, a resolver, an organizer, and a reporter [10,11]. The fractal architectural model represents a hierarchical structure built from the elements of a BFU, and the design of a basic unit incorporates a set of pertinent attributes that can fully represent any level in the hierarchy [12]. In other words, the term ‘fractal’ can represent an entire manufacturing shop at the highest level or a physical machine at the bottom-level. Each BFU provides services according to an individual-level goal and acts independently while attempting to achieve the shoplevel goal. An FrMS has many advantages for a distributed and dynamic manufacturing environment. Automatic reconfiguration of a system through a dynamic restructuring process (DRP) is the most distinctive characteristic of the FrMS. In this paper, the scope of the reconfiguration does not include reconfigurable hardware [15] and external layout design. Rather, it focuses on the interior structure of software components that can be reorganized with software manipulations. The reconfiguration or restructuring in this paper considers both dynamic clustering of the agents and construction/destruction/cloning of agents, which affect the number of agents in the system. The function of a fractal is not specifically designated at the time of its first installation in the FrMS. The reconfiguration addressed in this paper also includes situations where the agents’ enrollments are changed, meaning that the agents are assigned a new goal and new jobs, but their composition does not change. This paper focuses on formal modeling of agents and fractal-specific characteristics that will provide a foundation for the development of the FrMS. Because associated difficulties have, to date, prevented a fractal-based system from being embodied, it is necessary to first explicitly define a concept, mechanisms, and characteristics. The objective of this paper, therefore, is to clearly define and model fractal-specifc characteristics for a manufacturing system to have such characteristics. In order to develop the agents, interand intra-fractal activities are first clarified. Then, dynamic activities for each agent and relationships between agents are modeled. In order to more fully develop the FrMS, several fractal-specific characteristics are also modeled. To support embodiment of modeled characteristics, a method for dealing with information about products, orders, and resources in the FrMS is investigated. Through this research, mechanisms of agents and characteristics of the FrMS can be described with simple diagrams that make the system easier to understand. The work contained in this paper extends the FrMS from previous papers by emphasizing and detailing its characteristics. The activities of agents are specified using activity models so that the agents can use the activity models to forecast their next activities at run-time. The rest of this paper is organized as follows: Section 2 describes functions and dynamic activities of agents using functional and activity models of unified modeling language (UML). In Section 3, inter- and intra-fractal activities are specified. Several fractal-specific characteristics are described using UML models in Section 4. Section 5 describes a method for dealing with information about products and resources in the FrMS. Section 6 concludes the paper. 2. Agent-based fractal manufacturing system (FrMS) 2.1. Background of an FrMS An overview of the FrMS is depicted in Fig. 1. Every controller at every level in the system has a selfsimilar functional structure composed of functional modules. In addition, each of these modules, regardless of its hierarchical level, consists of a set of agents. After the initial setup of a system, the configuration of the system may need to be reorganized in response to unexpected events such as machine breakdown. The system will also need to be reconfigured when the set of parts to be produced in the system changes due to a change in customer needs. In these cases, fractals in the FrMS autonomously and dynamically change their structure, via the actions of agents for the appropriate working mechanisms of the fractals. Fig. 1 shows two facility layouts and the corresponding compositions of fractals before and after the restructuring process. When a machine (M) and a robot (R3) are added to the system, fractals reorganize their interior configurations with the mechanism of dynamic restructuring process in a way that the system continues to work with greatest efficiency. A fractal consists of five functional modules illustrated with their relationships in Fig. 2. The functions of each module can be defined depending upon the application domain. Fig. 1. Reorganization of the system using a dynamic restructuring process in the FrMS. However, when the target domain is determined, the main functions of each module will be consistent throughout the system. For example, the function of a resolver may be different depending upon whether it is defined for controlling a manufacturing system or for managing supply chains. However, the main function of a resolver in a manufacturing system is similar to other resolvers in that system regardless of their level in the hierarchy. A bottom-level fractal has similar functions to those of a conventional equipment controller in a SFCS. A fractal, which is directly connected to equipment (e.g. machine, robot, etc.), receives sensory signals of equipment and returns messages or commands. The function of an observer is to monitor the state of the unit, to receive messages and information from outer fractals, and to Fig. 2. Functional modules and relationships of a fractal in an FrMS. transmit composite information to correspondent fractals. The function of an analyzer is to analyze alternative job profiles with status information, to rate dispatching rules, and to simulate analyzed job profiles in real-time. The analyzer finally reports results to the resolver so that the resolver can use them to make decisions. A resolver plays the most important role in a fractal, generating job profiles, goal-formation processes, and decision-making processes. During goal-formation processes, the resolver may employ a variety of numerical optimization or heuristic techniques to optimize the fractal’s goal. If necessary, the resolver executes negotiations, cooperation, and coordination among fractals. The function of an organizer is to manage the fractal status and fractal addresses, particularly for dynamic restructuring processes. The organizer may use numerical optimization techniques to find an optimal configuration while reconfiguring fractals. The fractal status is used to select the best job profile among several alternatives, and the fractal address is used to find the physical address of the fractal (e.g. machine_name, port_number, etc.) on the network. The function of a reporter is to report results from all processes in a fractal to others. In the case of a bottom-level controller, the fractal is similar to a traditional equipment controller. Therefore, most of its messages are commands for controlling the hardware. 2.2. Agents in an FrMS Agent technology has been widely used for various applications including information filtering and gathering [16], knowledge management [17], supply chain management [18], manufacturing architecture, system and design [19–21]. While the features and characteristics of an agent vary depending on the application, some common features found across different applications are as follows: Autonomy: capability of controlling and acting for itself in order to achieve goals. Mobility: capability of migrating its location to other places (an agent with mobility is called a mobile agent, otherwise known as a software or stationary agent). Intelligence: capability of learning and solving problems. Cooperativeness: capability of helping others if requested and accepting helps from others. Adaptability: capability of being effectively used at various domains. Reliability: capability of dealing with unknown situations (disturbances) and continuing actions if committed, etc. The mobility of agents is a useful feature in a distributed and dynamic system. A mobile agent is not bound to the system where it begins execution. It can travel freely among the controllers in a network and transport itself from one system in a network to another. The following are some advantages of the use of mobile agents in a system [22]: (1) it reduces the network load, (2) it overcomes network latency, (3) it encapsulates protocols, (4) it executes asynchronously and autonomously, (5) it adapts dynamically, (6) it is naturally heterogeneous, and (7) it is robust and faulttolerant. The types and functions of agents that implement functional modules of an FrMS have been brie?y described, and their initial development has been published in the earlier literature [11]. This paper enhances and re?nes the previously defined types and functions of agents so that they can perform functions of fractals successfully in the system. The names, types, and functions of agents in the FrMS are described as follows. The terms ‘‘-M’’ and ‘‘-S’’ written after the abbreviated name of each agent represent mobile agents and software agents, respectively. 2.2.1. Agents for an observer Network monitoring agent (NMA-S): It monitors messages from other fractals through TCP/IP. It receives messages from the upper/same/lower-level fractals, such as requests for negotiations, negotiation replies, job orders, status information, etc. The NMA delivers those messages to the resolver or the analyzer. Equipment monitoring agent (EMA-S): It monitors messages directly coming from equipment through a serial communication protocol such as RS232/ 422. Information on the status of equipment including signals indicating the start and completion of jobs are detected by the EMA. However, the fractal need not directly control equipment if it is not included in a bottom-level. 2.2.2. Agents for an analyzer Schedule evaluation agent (SEA-S): A SEA evaluates job profiles generated by the resolver. It helps the resolver to select the best job profile with respect to the current situation of the fractal. Dispatching-rule rating agent (DRA-S): It chooses the best dispatching rule for achieving its goals among several rules, such as shortest processing time (SPT), earliest due date (EDD), and so on. Real-time simulation agent (RSA-S): It performs real-time simulations in the on-line state with the results of the analyzed job profiles and the best dispatching rule. The RSA reports the results of simulations to the resolver. 2.2.3. Agents for a resolver Schedule generation agent (SGA-M): It generates operational commands or alternative job profiles for achieving the fractal’s goals. After evaluation and analysis of alternatives in the analyzer, the SGA selects the best job profile. It must have mobility in order to use SEA, DRA, and RSA in the analyzer. Goal formation agent (GFA-S): It modifies incomplete goals delivered from the upper-level fractal, and tries to make the goals complete by considering the current situation of the fractal. GFA divides the goal of the fractal into several sub-goals, and sends them to the sub-fractals. Task governing agent (TGA-S): A TGA generates tasks from the best job profile and its goals. It also performs tasks after arriving at the target fractal. When it finishes performing tasks, it sends acknowledgement to its sender. Negotiation agent (NEA-M): It moves to other fractals to deliver negotiation messages or to gather negotiation replies created by participating agents. It filters out unreasonable replies by a pre-evaluation process and brings the rest back to the resolver. Knowledge database agent (KDA-M): KDA invokes knowledge data from the knowledge database to make decisions. It accumulates new knowledge or updates the existing knowledge. Decision-making agent (DMA-S): It performs several operations during the decision-making processes. A DMA creates NEAs to negotiate with other fractals and KDAs to use the knowledge database. After making decisions, the DMA generates several TGAs. Further, the DMA provides a context to agents for negotiation. 2.2.4. Agents for an organizer Fractal status manager (FSM-S): The FSM collects and manages the information on the status of fractals that is used for analyzing job profiles in the analyzer. It also makes negotiation replies to the status requests from other fractals. Fractal address manager (FAM-S): The FAM manages information about the addresses of fractals in lower levels and at the same level. A fractal address is the fractal’s physical address on the network, such as an IP address. The reporter uses a fractal’s address to confirm the destination of tasks and messages. Restructuring agent (REA-M): It performs several operations related to dynamic restructuring processes, such as BFU generation, BFU deletion, and the evaluation of the fractal’s performance. The performance of a fractal is its utilization, e.g. total number of processed jobs or the portion of processing time within total time, etc. If the REA decides that a fractal needs to be restructured, it gathers information about fractal and network addresses, and fractal status. It moves to the DMA and lets it generate a series of jobs for a restructuring process. The cloning mechanism is used to create a new BFU. After creation, the REA tells the FAM to update the addresses of other fractals. 2.2.5. Agents for a reporter Network command agent (NCA-M): All tasks or messages are deliv