自动化立体车库管理系统的研究(英文文献+CAD图纸)
usually used to indicate how many objects or classes may participate in a given relationship. For example, the uni-directional association between the DMA and the TGA has the stereotype of creates and two multiplicity values: 1 and 1, . . ., . It means that one DMA class can create more than one TGA class (the asterisk value represents any positive integer value). To simplify the class diagram, the attributes and operations of the classes are omitted in Fig. 3. Also, several other agents necessary for the FrMS are omitted to focus on the relationships between fractal agents. One of these classes (DMA) will be explained in great detail later in this paper. Each agent in a fractal has been modeled with a class diagram and an activity diagram. An activity diagram is used for de?ning speci?c activities and state transitions for classes. Fig. 4 illustrates the class diagram of the DMA, one of the agents in the resolver. Compared with the simpled version in Fig. 3, there are a few additional classes in Fig. 4, including the status information class, goals class, fractal performance evaluator class, and decision-making rule class.
Fig. 3. Class diagram of fractal agents.
Fig. 4. The class diagram of DMA.
The activities and transitions of the states of the DMA are modeled via the activity diagram in Fig. 5. A rectangle with rounded ends is used to de?ne an activity or a behavior of an object; a rounded rectangle is used to represent a state of an object in the activity diagram; and a diamond is used when a decision is needed. Transitions between actions or states are represented as an arrow. Transitions may have events, a stereotype, arguments, conditions, and actions with such UML syntax as ‘‘event(args)[condition]: Action’’. Transitions can be split by the decision and the applicable conditions. For example, in the case of the DMA (see Fig. 5), the next activity after executing the ‘‘Get input’’ activity can be one of six actions depending on the input received. Note that, as shown in Table 1, some conditions are represented by
 Fig. 5. The activity diagram of DMA.
 symbolic values (c0, c1, . . ., c8) to simplify the diagram. ha; b; ci indicates that one of a, b, or c is a prerequisite ?ow for the condition, and [a] means that the condition cannot be applicable after a. Other logic (activities, states, decisions, and transitions) in Fig. 5 can be inferred from the English in the figure.
3. Activities of agents in the FrMS
In the FrMS, agents handle all processes and jobs without humanintervention. Some activities are processed within the fractal, while other activities require cooperation with other agents that exist in another fractal. Activities of agents are classified into two categories: intra-fractal activity (the activities that are processed in one fractal) and inter-fractal activity (the activities that are processed by the cooperation of several fractals). The classification of activities of agents in the FrMS is summarized in Fig. 6. Most fractal-specific characteristics are related to inter-fractal activities such as negotiation, goal-orientation, and the dynamic restructuring process.
3.1. Intra-fractal activity In order to control an FrMS, agents are involved in processing jobs with their specific roles. The activities of agents that are performed within a fractal are similar to those of other manufacturing systems including input/output control, scheduling, task generation, performance of tasks, and equipment control. Inputs from other fractals and equipment are controlled by the NMA and EMA, respectively. Many agents must cooperate for scheduling activities. The agents dealing with scheduling processes include: (1) SEA, DRA, and RSA in an analyzer, (2) TGA and DMA in a resolver, and (3) FSM in an organizer. The DMA first creates the alternative job profiles, and the SEA evaluates them. After the DRA selects the best dispatching rule with respect to the status and goals of the fractal, the RSA scores the evaluated job pro?les using real-time simulation. The DMA gets the results of the simulation from the RSA to generate TGAs. If a negotiation with other fractals is needed during the scheduling process, the DMA creates NEAs to gather the necessary information. After completing the schedule, each TGA generates tasks. Regarding the management of information, the FSM manages fractal status information, the FAM manages fractal addresses, and the STA keeps the speci?cations of controllers. The task executor in each TGA accomplishes tasks assigned to a particular fractal. The DMA interacts with a knowledge database and uses queried knowledge to make decisions by creating KDAs. A fractal has an EMA and an ECA to control the equipment in the system. The EMA monitors sensory signals

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