Faunhofer Project Center for Production Management and Informatics PMI

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  • Publication
    Manufacturing lead time estimation with the combination of simulation and statistical learning methods
    ( 2016)
    Pfeiffer, A.
    ;
    Gyulai, D.
    ;
    Kádár, B.
    ;
    Monostori, L.
    In the paper, a novel method is introduced for selecting tuning parameters improving accuracy and robustness for multi-model based prediction of manufacturing lead times. Prediction is made by setting up models using statistical learning methods (multivariate regression); trained, validated and tested on log data gathered by manufacturing execution systems (MES). Relevant features, i.e.; the predictors most contributing to the response, are selected from a wider range of system parameters. The proposed method is tested on data provided by a discrete event simulation model (as a part of a simulation-based prediction framework) of a small-sized flow-shop system. Accordingly, log data are generated by simulation experiments, substituting the function of a MES system, while considering several different system settings (e.g.; job arrival rate, test rejection rate).
  • Publication
    Simulation-based production planning and execution control for reconfigurable assembly cells
    ( 2016)
    Gyulai, D.
    ;
    Pfeiffer, A.
    ;
    Kádár, B.
    ;
    Monostori, L.
    In order to meet the continuously changing market conditions and achieve economy of scale, a current trend in the automotive industry is the application of modular reconfigurable assembly systems. Although they offer efficient solution to meet the customers needs, the management of these systems is often a challenging issue, as the continuous advance in the assembly technology introduces new requirements in production planning and control activities. In the paper, a novel approach is introduced that enables the faster introduction of modular assembly cells in the daily production by offering a flexible platform for evaluating the system performance considering dynamic logistics and production environment. The method is aimed at evaluating different modular cell configurations with discrete-event simulation, applying automated model building and centralized simulation model control. Besides, the simulation is linked with the production and capacity planning model of the system in order to implement a cyclic workflow to plan the production and evaluate the system performance in a proactive way, before releasing the plan to the production. The method and the implemented workflow are evaluated within a real case study from the automotive industry.
  • Publication
    Supporting multi-level and robust production planning and execution
    ( 2015)
    Stricker, N.
    ;
    Pfeiffer, A.
    ;
    Moser, E.
    ;
    Kádár, B.
    ;
    Lanza, G.
    ;
    Monostori, L.
    Operating current production systems influenced by the factors of increasing dynamics and volatility poses a need for robustness. Among different enablers for robustness the appropriate ones for specific production systems have to be identified and evaluated. In this cooperative paper multi-objective decision support models will be presented evaluating the best enablers for the levels of production network, plant and shop-floor. The suggested models for the stabilization of the production system's performance under volatile environment use analytical and simulation based approaches on the regarded levels.