自动化立体车库管理系统的研究(英文文献+CAD图纸)
systems include (1) self-similarity, (2) self-organization, and (3) goal-orientation. Speci?cally, the dynamic restructuring process, which is part of self-organization, is the most distinctive characteristic. This section describes fractal-speci?c characteristics with respect to UML models, focusing on their procedures and the relationships among participating agents.
 4.1. Self-similarity
Self-similarity, an inherent characteristic of a fractal, refers not only to the structural characteristics of organizational design, but circumscribes the manner of performing a job (service), as well as the formulation and pursuit of goals [13]. To achieve goals in a manufacturing system, there can be various possible solutions with respect to the individual problems. Even if there may exist several components with the same goal in the system, conditions or situations in the surrounding environment may be different for each component. This can result in fractals that have identical goals, while their input and output variables have quite different internal structures. If two fractals return the same outputs for the same inputs, they are called ‘‘self-similar’’, even if their internal structures are different. This characteristic can be affirmatively used to develop control software in the design phase because control modules or agents can be generated from the common structures. Therefore, a fractal designed at one level can be applied to other levels in the FrMS because of the self-similarity of fractals.
 4.2. Self-organization
 Self-organization is related to a theoretical method and an operational method in the FrMS. The theoretical method referred to as self-optimization is de?ned as the application of suitable numerical approaches to optimize the performance of fractals in a system. It provides the FrMS with a mathematical background for designing the structures, compositions, and relationships of fractals. From various optimization techniques, fractals select and use a proper method to have a more optimal specification. Details on the optimization techniques are beyond the scope of this paper. The dynamic restructuring process (operational method) supports the reorganization of network connections between fractals so that the FrMS can be optimized and adapted to a dynamically changing environment. The DRP continuously changes the structure of the whole system depending on the fractals’ goals and external environmental conditions. For example, it is supposed that an unexpected event causes a controller to malfunction, or the type of parts that have to be produced in a system changes. In that case, controllers need to be changed or reorganized. The FrMS can perform these tasks automatically and dynamically with little intervention from human operators by using the DRP. The REA in an organizer leads the DRP. The activities of the DRP are modeled by using an activity diagram as illustrated in Fig. 8. If the REA decides to perform the DRP based on the results of periodic evaluations of a fractal’s performance, it first makes a new structure of fractals by employing a resource optimizer. The REA also employs the DMA if it needs to negotiate with other fractals. The REA sends a request for the address information to a NTA. It also sends a request for the specification for a new controller to the FSM if the system needs more controllers. Then the REA sends a request to the FAM to get the addresses of fractals associated with the DRP. The REA creates a restructuring message, and informs the DMA of the DRP to make it generate the series of jobs for restructuring the fractals. The task executor in each of the TGAs conducts DRP-related jobs. Finally, the REA informs the FAM that fractal address information must be updated before the DRP can finish.
4.3. Goal-orientation
 Goal-orientation corresponds to the motivated activities of agents in fractals. To coherently achieve agents’ and fractals’ goals during the process, goal consistency, which is supported by an inheritance mechanism, should be maintained. The FrMS continues to develop each goal autonomously in order to operate and harmonize the system by resolving confiicts. Basically, efficient production may be a usual goal. In accordance with the surrounding environment, however, a goal may

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