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Tytuł artykułu

The impact of the availability of resources, the allocation of buffers and number of workers on the effectiveness of an assembly manufacturing system

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Języki publikacji
EN
Abstrakty
EN
This paper proposes the application of computer simulation methods in order to analyse the availability of resources, buffers and the impact of the allocation of workers on the throughput and work-in-progress of a manufacturing system. The simulation model of the production system is based on an existing example of a manufacturing company in the automotive industry. The manufacturing system includes both machining and assembly operations. Simulation experiments were conducted vis-`a-vis the availability of the different manufacturing resources, the various allocations of buffer capacities and the number of employees. The production system consists of three manufacturing cells – each cell including two CNC machines – and two assembly stations. The parts produced by the manufacturing cells are stored in buffers and transferred to the assembly stations. Workers are allocated to the manufacturing cells and assembly stations, but the number of workers may be less than number of workplaces and are thus termed ‘multi-workstations’. Using computer simulation methods, the impact of the availability of resources, the number of employees and of the allocation of buffer capacity on the throughput and work-in-progress of the manufacturing system is analysed. The results of the research are used to improve the effectiveness of manufacturing systems using a decision support system and the proper control of resources. Literature analysis shows that the study of the impact of buffer capacities, availability of resources and the number of employees on assembly manufacturing system performance have not been carried out so far.
Twórcy
autor
  • University of Zielona Góra, Institute of Management and Production Engineering, Prof. Szafrana 4, 65-526 Zielona Góra, Poland
autor
  • Technical University of Kosice, Faculty of Mechanical Engineering, Slovakia
Bibliografia
  • [1] Negahban A., Smith J.S., Simulation for manufacturing system design and operation: literature review and analysis, Journal of Manufacturing Systems, 33, 241–261, 2014.
  • [2] Jahangirian M., Eldabi T., Naseer A., Stergioulas L.K., Young T., Simulation in manufacturing and business: a review, European Journal of Operational Research, 203, 1, 1–13, 2010.
  • [3] Jagstam M., Klingstam P., A handbook for integrating discrete event simulation as an aid in conceptual design of manufacturing systems, Proceedings of the 2002 Winter Simulation Conference, 2, 1940–4, 2002.
  • [4] Lim J.K., Lim J.M., Yoshimoto K., Kim K.H., Takahashi T., A construction algorithm for designing guide paths for automated guided vehicle systems, International Journal of Production Research, 40, 15, 3981–94, 2002.
  • [5] Um I., Cheon H., Lee H., The simulation design and analysis of a flexible manufacturing system with automated guided vehicle system, Journal of Manufacturing Systems, 28, 4, 115–22, 2009.
  • [6] Vasudevan K., Lammers E., Williams E., Ulgen O., Application of simulation to the design and operation of steel mills devoted to the manufacture of pipelines, Second International Conference on Advances in System Simulation (SIMUL), pp. 1–6, 2010.
  • [7] Jithavech I., Krishnan K., A simulation-based approach to the risk assessment of facility layout designs under stochastic product demands, The International Journal of Advanced Manufacturing Technology, 49, 27–40, 2010.
  • [8] Yang T., Zhang D., Chen B., Li S., Research on plant layout and production line running simulation in digital factory environment, Pacific-Asia Workshop on Computational Intelligence and Industrial Application, 2, 588–593, 2008.
  • [9] Reeb J., Baker E., Brunner C., Funk J., Reiter W., Using simulation to select part-families for cell manufacturing, International Wood Products Journal, 1, 1, 43–7, 2010.
  • [10] Joseph O.A., Sridharan R., Simulation modelling and analysis of routing flexibility of a flexible manufacturing system, International Journal of Industrial and Systems Engineering, 8, 1, 61–82, 2011.
  • [11] Azadeh A., Anvari M., Ziaei B., Sadeghi K., An integrated fuzzy DEA-fuzzy-means-simulation for optimisation of operator allocation in cellular manufacturing systems, The International Journal of Advanced Manufacturing Technology, 46, 1–4, 361–75, 2010.
  • [12] Hsueh C.F., A simulation study of a bi-directional load-exchangeable automated guided vehicle system, Computers & Industrial Engineering, 58, 4, 594– 601, 2010.
  • [13] Vidalis M.I., Papadopoulos C.T., Heavey C., On the workload and ‘phase load’ allocation problems of short reliable production lines with finite buffers, Computers and Industrial Engineering, 48, 825–837, 2005.
  • [14] Battini D., Persona A., Regattieri A., Buffer size design linked to reliability performance: a simulative study, Computers & Industrial Engineering, 56, 1633–1641, 2009.
  • [15] Demir L., Tunali S., Eliiyi D.T., The state of the art on the buffer allocation problem: a comprehensive survey, Journal of Intelligent Manufacturing, 25, 371–392, 2014.
  • [16] Staley D.R., Kim D.S., Experimental results for the allocation of buffers in closed serial production lines, International Journal of Production Economics, 137, 284–291, 2012.
  • [17] Tecnomatix Plant Simulation 11 – on-line documentation.
  • [18] Kłos S., Patalas-Maliszewska J., Trebuňa P., Improving manufacturing processes using simulation methods, Applied Computer Science, 12, 4, 7–17, 2016.
  • [19] Kłos S., Trebuňa P., Using computer simulation method to improve throughput of production systems by buffers and workers allocation, Management and Production Engineering Review, 6, 4, 60–69, 2015.
  • [20] Kłos S., The simulation of manufacturing systems with tecnomatix plant simulation, Zielona Góra, Oficyna Wydaw. Uniwersytetu Zielonogórskiego, pp. 124, 2017.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-571faf4a-cf41-4c25-8b08-8b44e4b0a7ff
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