Faunhofer Project Center for Production Management and Informatics PMI

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  • Publication
    Scheduling and Operator Control in Reconfigurable Assembly Systems
    ( 2017)
    Gyulai, D.
    ;
    Kádár, B.
    ;
    Monostori, L.
    Pushed by the recent market trends, companies need to adapt to changeable demands, regarding both mix and volume, in order to keep their competitiveness. Modular and reconfigurable assembly systems offer an efficient solution to these changes, providing economies of scale and also economies of scope. In the previous works of the authors, novel methods were presented to solve strategic level system configuration, and tactical mid-term production planning problems related to modular, reconfigurable assembly systems. The paper relies on these results, and aims at extending the previously proposed planning hierarchy on the short-term, daily production scheduling. The objective is to minimize the total operator headcount, considering the production lot sizes calculated on a higher, planning level on a working shift basis. The analyzed scheduling problem requires novel models, as important constraints in the scheduling problem are the reconfigurations consuming time as well a s resources. In the paper, constraint programming and metaheuristics models are formulated and compared, resulting in production schedules that specify the production sequences, and the operator allocations. Conclusively, the operator controls can be also obtained from the results, specifying a work plan and tasks for a given operator within a working shift. The proposed methods are compared by using real industrial problem instances.
  • 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
    Robust production planning and capacity control for flexible assembly lines
    ( 2015)
    Gyulai, D.
    ;
    Kádár, B.
    ;
    Monosotori, L.
    The frequently changing order stream and high product variety require robust planning and control approaches, as well as a flexible system structure in order to fulfill the highest possible customer service level and to keep the production costs on a reasonable level. In the paper, a combined production planning and capacity control method for assembly lines is proposed aiming at balancing the workload of the human operators and decreasing the overall production costs on a given time horizon. Instead of using the idealistic cycle times and simple manufacturing control rules, the proposed planning and control methodology is based on adaptive calculations taken from continuously updated historical production data. The manufacturing execution-level data is applied for building regression models predicting the capacity requirements of the future production scenarios. Besides, the historical data is also used as direct input of discrete-event simulations, to determine the proper control policies of human operator allocations for the different scenarios mentioned above. In order to calculate reliable and feasible production plans, the regression models and control policies are integrated in a mathematical programming model that minimizes a cost function representing the total production costs.