A hierarchical fuzzy rule-based building model applied to a AGV dispatching system in an FMS

Abstract

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. Material handling is a key component in reaching flexibility, manufacturing, dynamism and agility goals. 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 a heuristic dispatching Automation Guided Vehicle (AGV) based on multiple attributes and fuzzy logic. Typically, the description of the rule base corresponds to all the possibilities of the connection between the input and output. The manual setting up of the rule base for a determined set of variables is not feasible when considering the efficiency the rules can offer in terms of performance during the inference process. It is also important to note that even for an expert in the field, the task of considering the quantitative value of the correct contribution where each rule affects the system becomes a difficult task. Taking this into account, this paper uses an approach to reduce the rules to a method of hierarchical fuzzy rules. As a means of evaluation, the proposed method is applied to a model based on fuzzy rules developed and used in AGV dispatching of a Flexible Manufacturing System (FMS).

Sistema Fuzzy Para Tomada De Decisão De Sistemas Multiagentes

“…Tal controlador define oângulo de cada junta do robô. Em 2011, Morandin et al [6] aplicaram um sistema fuzzy para definir a prioridade de atendimento do pedido de recursos feitos a um robô em um ambiente de manufatura. Já Wu e Lee [7] aplicaram um sistema fuzzy para a tomada de decisões de qual comportamento cada robô deveria tomar.…”