Faunhofer Project Center for Production Management and Informatics PMI

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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.

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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.

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Execution data connected simulation for production operation planning and control

2012 , Kádár, B. , Pfeiffer, A. , Monostori, L.

Due to the large number of the resources, the frequent reengineering of manufacturing processes and the often changing product types, the maintenance of the simulation model and the provision of up-to-date input data are almost always difficult. The paper introduces new quantitative analysis method including processing time and batch calculation algorithms applied in the automatic building of discrete-event simulation systems. The simulation model and the mentioned algorithms are both directly interfaced with real execution databases and the overall solution supports the shop-floor managers in making control decisions. The applicability of the elaborated approach is illustrated by the results of experiments relying on real historical data.

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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).

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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.

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Novel it solutions for increasing transparency in production and in supply chains

2011 , Monostori, L. , Ilie-Zudor, E. , Kádár, B. , Karnok, D. , Pfeiffer, A. , Kemény, Zs. , Szathmári, M.

The paper summarises the main challenges and problems related to transparency in production firms and networks. The transparency problem of production are treated from three aspects, i.e., factory level data gathering tight-coupled with productions simulation; extracting knowledge from large, complex, time-dependent noisy and anomalous process logs; and identity-based tracking and tracing services within and beyond organizational borders. For all these fields of industrial relevance novel approaches and solutions are presented.

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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.

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Automatic simulation model generation supported by data stored in low level controllers

2012 , Popovics, G. , Kardos, C. , Pfeiffer, A. , Kádár, B. , Vén, Z. , Monostori, L.

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 needs great resource expenditures. Automated data collection and model buildup can drastically reduce the time of the design phase as well as support model reusability. Since most of the manufacturing systems are controlled by low level controllers (e.g., PLCs, CNCs) they store structure and control logic of the system to be modeled by a DES system. The paper introduces an ongoing research of PLC program processing method for automatic simulation model generation of a conveyor system of a leading automotive factory. Results of the validation process and simulation experiments are also described through a case study.

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