Faunhofer Project Center for Production Management and Informatics PMI

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  • Publication
    An integrated framework for design, management and operation of reconfigurable assembly systems
    ( 2018)
    Manzini, M.
    ;
    Unglert, J.
    ;
    Gyulai, D.
    ;
    Colledani, M.
    ;
    Jauregui-Becker, J.M.
    ;
    Monostori, L.
    ;
    Urgo, M.
    Manufacturing has to cope with the continuously increasing variety of products, change of volumes and shortening product life cycles. These trends also affect the automotive sector: the frequent introduction of new models, materials and assembly technologies put the suppliers of make-to-order parts under pressure. In this context, the design of assembly systems and their management are of paramount importance for the companies' competitiveness. In this paper, we propose an approach for the design and reconfiguration of modular assembly systems through the integration of different computational tools addressing the design of the system, the optimization of the layout, the planning of reconfiguration actions as well as production planning. Integrating these computational tools and iterating through the resulting workflow and feedback allow to consider the outcomes and dependencies of alternative decision sequences holistically with the objective of an effective and efficient approach to production system design and management. The viability of the approach is demonstrated through the application to an automotive case study.
  • Publication
    Capacity management of modular assembly systems
    ( 2017)
    Gyulai, D.
    ;
    Monostori, L.
    Companies handling large product portfolio often face challenges that stem from market dynamics. Therefore, in production management, efficient planning approaches are required that are able to cope with the variability of the order stream to maintain the desired rate of production. Modular assembly systems offer a flexible approach to react to these changes, however, there is no all-encompassing methodology yet to support long and medium term capacity management of these systems. The paper introduces a novel method for the management of product variety in assembly systems, by applying a new conceptual framework that supports the periodic revision of the capacity allocation and determines the proper system configuration. The framework has a hierarchical structure to support the capacity and production planning of the modular assembly systems both on the long and medium term horizons. On the higher level, a system configuration problem is solved to assign the product families to dedicated, flexible or reconfigurable resources, considering the uncertainty of the demand volumes. The lower level in the hierarchy ensures the cost optimal production planning of the system by optimizing the lot sizes as well as the required number of resources. The efficiency of the proposed methodology is demonstrated through the results of an industrial case study from the automotive sector.
  • Publication
    Robust production planning and control for multi-stage systems with flexible final assembly lines
    ( 2017)
    Gyulai, D.
    ;
    Pfeiffer, A.
    ;
    Monostori, L.
    Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operatorâtask assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components.
  • 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
    Design and management of reconfigurable assembly lines in the automotive industry
    ( 2016)
    Colledani, M.
    ;
    Gyulai, D.
    ;
    Monostori, L.
    ;
    Urgo, M.
    ;
    Unglert, J.
    ;
    Houten, F. van
    Automotive suppliers are facing the challenge of continuously adapting their production targets to variable demand requirements due to the frequent introduction of new model variants, materials and assembly technologies. In this context, the profitable management of the product, process and system co-evolution is of paramount importance for the company competitiveness. In this paper, a methodology for the design and reconfiguration management of modular assembly systems is proposed. It addresses the selection of the technological modules, their integration in the assembly cell, and the reconfiguration policies to handle volume and lot size variability. The results are demonstrated in a real automotive case study.
  • 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
    Capacity planning and resource allocation in assembly systems consisting of dedicated and reconfigurable lines
    ( 2014)
    Gyulai, D.
    ;
    Kádár, B.
    ;
    Monostori, L.
    Companies with diverse product portfolio often face capacity planning problems due to the diversity of the products and the fluctuation of the order stream. High volume products can be produced cost-efficiently in dedicated assembly lines, but the assembly of low-volume products in such lines involves high idle times and operation costs. Reconfigurable assembly lines offer reasonable solution for the problem; however, it is still complicated to identify the set of products which are worth to assemble in such a line instead of dedicated ones. In the paper a novel method is introduced that supports the long-term decision to relocate the assembly of a product with decreasing demand from a dedicated to a reconfigurable line, based on the calculated investment and operational costs. In order to handle the complex aspects of the planning problem a new approach is proposed that combines discrete-event simulation and machine learning techniques. The feasibility of the approach is demonstrated through the results of an industrial case study.