Now showing 1 - 3 of 3
  • Publication
    Methodology and data-structure for a uniform system's specification in simulation projects
    ( 2013)
    Kardos, Csaba
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    Popovics, Gergely
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    Kádár, Botond
    ;
    Monostori, László
    In the last few decades the evaluation and analysis of manufacturing systems' behavior and their performance became very important. Digital enterprise technologies, as for example discrete-event simulation (DES), are effective tools both in production related decision making processes and in structure and performance analysis of manufacturing systems. However, building a discrete-event based simulation model of a manufacturing system is a difficult task and requires special competence. The majority of simulation studies are aimed at analyzing a certain problem by a specific simulation model created by experts with a relatively high financial expenditure. The paper introduces an ongoing research aimed at developing a framework to reduce the efforts spent on draft simulation studies by simplifying and accelerating the process of model building. The proposed modeling methodology uses a uniform data structure which is a production oriented implementation of the ANSI/ISA-95 standard and supports the creation of models without simulation software specific knowledge. The supporting data structure enables the development and application of proprietary simulation engines tailored for specific problems. The paper compares the traditional and the proposed methodologies and also introduces the first experiments gained on specific test -cases. In our approach the simulation models are created automatically and independently from simulation tools which will be presented through the examples of both commercial and self-developed applications.
  • Publication
    Uniform Data Structure for Production Simulation
    ( 2012)
    Popovics, Gergely
    ;
    Kardos, Csaba
    ;
    Kemény, Lorinc
    ;
    Gyulai, Dávid
    ;
    Monostori, László
    One of the most widespread techniques to evaluate various aspects of a manufacturing system is discrete-event simulation (DES). However, building a simulation model of a manufacturing system is a difficult task and requires special competence. The majority of simulation studies aimed at analyzing a certain problem by a specific simulation model created by experts. This approach requires relatively high financial expenditures. The paper introduces an ongoing research aimed at developing an application to reduce the costs of a draft simulation study. The input of the simulation models in the proposed methodology is based on ISA-95 standardized data structure. The instantiated input data set can be stored both in MS Excel, XML files or an SQL database. Having the complete input a simulation model can be created automatically without simulation expertise.
  • Publication
    Applying model-reconstruction by exploring MES and PLC data for simulation support of production systems
    ( 2012)
    Pfeiffer, András
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    Kádár, Botond
    ;
    Popovics, Gergely
    ;
    Kardos, Csaba
    ;
    Vén, Zoltán
    ;
    Kemény, Lorinc
    ;
    Monostori, László
    The paper introduces a discrete-event simulation-based decision supporting system aiming at automatically mirroring the current state of a large-scale material handling system of a production system in a digital model and supporting the analysis of diverse control settings and rules. The discrete-event digital model is built in an automated way and all the data necessary for the model are taken from a manufacturing execution system (MES) and additionally directly from programmable logic controllers (PLC). Main focus is given to present the results of the PLC program code processing method (code mapping) generating a structured dataset, as well as the model-reconstruction method for the simulation software. The easy-of-use support tool is intended to be used both in planning and operation phases of an automotive manufacturing company, thus the capabilities of model reconstruction and simulation are tested on real-world data.