Scientific and Technical Journal

ELECTROTECHNIC AND COMPUTER SYSTEMS

ISSN Print 2221-3937
ISSN Online 2221-3805
APPLICATION OF DISCRETE-EVENT SIMULATION TO IMPROVE LOGISTICS SYSTEM ASSEMBLING DEPARTMENT
Abstract:
The purpose of this paper is to improve the efficiency of logistics system assembly area for washing machines. The article deals with discrete event simulation method of the production process, which breaks through the logistics flow on individual related operations and allows us to consider each of them separately. Experiment was conducted, during which, on the basis of compiled simulation model of the logistics process in the program AnyLogic, it was determined the effect of qualitative and quantitative changes in production capacity of the site to the performance of its operations, assembly and packaging, as well as the overall performance of the whole process. The novelty of this work lies in the fact that the simulation model accurately predicts the impact of logistics solutions for the performance of the assembly area, without affecting the actual production process. The created model describes in detail the work of the assembly and packaging workshops, shows ways to increase the performance of the site and can be applied to existing industries and for planning new ones.
Authors:
Keywords
DOI
10.15276/etks.18.94.2015.12
References
1. Savin V.I. Organizaciya skladskoj deyatelnosti: Spravochnoe posobie, 2-e izd., [Organization of Warehouse Activities: a Guidebook], (2007), Moscow, Russian Federa-tion, Izdatelstwo “Delo i Servis”, 544 p. (In Russian).
2. Imitacionogo modelirowaniya [Methods of Simulation Modeling ion: Metody], (In Rus-sian) [Electronic Source], Proizwodstwo, avail-able at: http://www.anylogic.de/discrete-event-simulation.html (accessed 27.03.2015).
3. Carone M. Using Modeling and Simula-tion to Test Designs and Requirements. Math-Works News&Notes. The Magazine for the MATLAB and Simulink Community, No. 10, 2014, pp. 26 – 29.
4. Perl J., (2010), Net-based Phase-Analysis in Motion Processes. Mathematical and Computer Modelling of Dynamical Systems, Vol. 16, No. 5, October 2010, pp. 465 – 475.
5. Fortuna L., Graziani S., Rizzo A., and Xibilia M., (2007), Soft Sensors for Monitoring and Control of Industrial Processes: Advances in Industrial Control. New York: Springer, 270 p.
6. Bicher M., Music G., Hafner I., and Bre-itenecker F., (2014), Support of Event-Graph Lectures by the MMT E-learning System, SNE Educational Note, Vol. 24, No. 1, April 2014, pp. 47 – 50.
7. Leskovar R., Tanzler J., and Bicher M., (2014), Petri Net Modelling and Simulation in AnyLogic and MATLAB for ARGESIM Benchmark C4 “Dining Philosophers”. SNE Educational Note, Vol. 24, No. 1, April 2014, pp. 55 – 58.
8. Obermair M., and Glock B., (2014), Agent-based Simulation of the Railway Connec-tion from and to the Vienna International Air-port. SNE Educational Note, Vol. 24, No. 3 – 4, Dec. 2014, pp. 123 – 126.
9. Glock B, and Breitenecker F., (2014), Ein System Dynamics Modell zur Prävalenz von Adipositas in Österreich, Master Thesis, Institute for Analysis and Scientific Computing. – Österreich, Vienna.
10. Breitenecker F., and Popper N., (2009), Classification and Evaluation of Features in Advanced Simulators, MATHMOD 09, Öster-reich, Vienna, No. 1.
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2017-11-17 01:02:49

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