Scientific and Technical Journal


ISSN Print 2221-3937
ISSN Online 2221-3805
In this article the management of warehouse operations, taking into account all the parameters of the warehouse, its resources and the dynamics of movement of the product. The aim of this work is to improve the performance of the logistics system of an industrial warehouse. To do this, we used the method of network modeling and simulation model created logistic warehouse. This simulation model has been developed to optimize the warehouse and takes into account the material with arrival of goods, including changes in volume and time of arrival. It fully describes all warehouse operations and allows us to consider the specifics of the warehouse at both the macro and micro levels, over a long period of time and can be used in warehouse logistics. By conducting an experiment on the compiled simulation model has been defined as quantitative changes in production capacity logistic warehouse operations will affect the performance of its sites, as well as the overall performance of warehouse logistics flow. The relevance of this article lies in the fact that today the competition in the warehouse activity is very high and requires constant development of the logistics system stores. The created model shows the effect of the applied solutions and predicts the outcome of their implementation for many years to come.
1.Gadzhinskiy A. M. Logistika: Uchebnik, [Logistics: Textbook: 20 ed], (2012), Moscow, Russian Federation, Izdatelsko-torgovaya kor-poratsiya “Dashkov i K”, 484 p. (In Russian).
2. Savin V.I. Organizaciya skladskoj deyatelnosti: Spravochnoe posobie [Organiza-tion of Warehouse Operations: Handbook: 2nd ed], (2007), Moscow, Russian Federation, Iz-datelstwo “Delo i Servis”, 544 p. (In Russian).
3. Metody imitacionogo modelirowaniya, [Methods of Simulation Modeling of] [Elec-tronic Source] (In Russian), Proizwodstwo, available at: dostupa: (accessed 27.03.2015).
4. Drozdova M., and Zboril F. (2010), Simulation of Queuing Systems using QS_PN_Simulation Tool. SNE Educational Note, Vol. 20, No. 1, April 2010, pp. 35 – 37.
5. Petzold M., Ullrich O., and Speckenmeyer E., (2010), Dynamic Distributed Simulation of DEVS Models on the OSGi Service. SNE Educational Note, Vol. 21, No. 3-4, December 2011, pp. 159 – 165.
6. 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.
7. Quiros G., Jorewitz R., Epple U., (2011), Model-based Safety Monitoring of Produkt Flow Paths. SNE Educational Note, Vol. 21, No. 1, April 2011, pp. 27 – 37.
8. Durak U., Schmidt A., and Pawletta T., (2014), Ontology for Objective Flight Simulator Fidelity Evaluation. SNE Educational Note, Vol. 24, No. 2, Aug. 2014, pp. 69 – 79.
9. Heinzl B., Rößler M., Popper N., Land-siedl M., Dimitriou A., and Breitenecker F., (2012), Object-Oriented Modelling for Energy-efficient Production. SNE Educational Note, Vol. 22, No. 1, April 2012, pp. 33 – 39.
10. Heinzl B., Auer E., Slowacki B., Kowarik K., Reschreiter H., Popper N., and Breitenecker F., (2012), Physical Modelling for Hallstatt Archaeology. SNE Educational Note, Vol. 22, No. 1, April 2012, pp. 25 – 33.
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15 Jan 2020

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