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Approaches of fuzzy systems applied to an AGV dispatching system in a FMS

机译:模糊系统应用于FMS AGV调度系统的方法

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Excellence in manufacturing systems has been recognized as one of the main factors behind the success of industrial companies or production companies. New technology for manufacturing processes plays a significant role in this process. Achieving the potential of technological innovations in production, however, requires a wide range of management, as well as engineering issues related to the system. The handling capacity of advanced materials is essential because without this ability of providing the material needed for the proper workstation at the right time and in the right amount, the whole plant will become "bogged down." This makes it less efficient and thus produces less profit, and/or it operates at higher costs. This paper proposes two approaches for the dispatching of AGV (automated guided vehicle) using systems fuzzy. The first use hierarchical fuzzy rule-based model building in which the main feature is to make the base of fuzzy rules is smaller and simpler but with high coverage and interpretability. The second use adaptive genetic fuzzy system with simple prediction in which the main feature is to increase the sensitivity of the system about the variables. Both approaches using multiple attributes and having the objective decrease the makespan in a FMS (flexible manufacturing system).
机译:制造系统的卓越性已被公认为是工业公司或生产公司成功的主要因素之一。制造工艺的新技术在此过程中起着重要作用。但是,要实现生产中技术创新的潜力,就需要广泛的管理以及与系统相关的工程问题。先进材料的处理能力至关重要,因为如果没有这种能力,无法在正确的时间和正确的数量提供适当的工作站所需的材料,整个工厂将陷入困境。这使其效率降低,从而产生较少的利润,和/或其成本更高。本文提出了两种基于模糊系统的自动导引车调度方法。首次使用基于层次模糊规则的模型构建,其主要特征是使模糊规则的基础更小,更简单,但具有较高的覆盖率和可解释性。第二种使用具有简单预测的自适应遗传模糊系统,其主要特征是提高系统对变量的敏感性。两种使用多种属性并具有目标的方法都降低了FMS(柔性制造系统)的制造期。

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