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Possibilities of more efficient use of simulation tools in enterprise logistics

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Języki publikacji
EN
Abstrakty
EN
Computer simulation, as a powerful scientific and engineering tool, is increasingly used to solve a wide range of issues within the enterprise logistics processes. This trend is mainly due to the fact that the computer simulation method offers a whole range of benefits for enterprise logistics e.g. saving time, space and finance. The use of computer simulation in logistics, however, still has certain limitations that handicap it and do not allow it to be more operationally exploited. The main reason is that the creation of models of different logistics processes is exacting on the skills and knowledge in this area. The factors as the difficulty of creating new and modifying existing models or repeating simulation experiments complicate the more frequent application of computer simulation models in logistics. For this reason, it is necessary to look for a way to simplify and make the issue available to a larger circle of users.
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Twórcy
autor
  • Technical University of Košice, Letná 9, 042 00 Košice, Slovak Republic
autor
  • Technical University of Košice, Letná 9, 042 00 Košice, Slovak Republic
autor
  • Technical University of Košice, Letná 9, 042 00 Košice, Slovak Republic
autor
  • Institute of Technology and Business in Česke Budějovice, Okružní 517/10, 370 01 české Budějovice, Czech Republic
autor
  • GW Train Regio a.s. , U stanice 827/9, 400 03 Ústi nad Labem, Czech Republic
  • College of Logistics in Přerov, Palackého 1381/25, 750 02 Přerov, Czech Republic
Bibliografia
  • 1.Strohmandl J. Use of simulation to reduction of faulty products. UPB Sci. Bull. Ser. D Mech. Eng., 76, 2014, 223–230.
  • 2.Klozíková J. and Dočkalíková I. Decision Support in Rating of the Level of Corporate Governance Using the WINGS Method. In: 11 th European Conference on Management, Leadership and Governance, ECMLG 2015. 569 – 579, 2015.
  • 3.Nedeliakova E., Sekulova J., Nedeliak I. and Abramovic B. Application of raymond fisk model in researchof service quality. Komunikacie 2 (18), 2016, 11-14.
  • 4.Fabianová J., Kačmáry P., Molnár V. and Michalik P. Using a Software Tool in Forecasting: a Case Study of Sales Forecasting Taking into Account Data Uncertainty. Open Eng., 6 (1), 2016, 270-279.
  • 5.Bergmann S., Feldkamp N. and Strassburger S. Journal of Simulation 11, 2017, 38–50.
  • 6.Chankov S.M., Malloy G. and Bendul J. The Influence of Manufacturing System Characteristics on the Emergence of Logistics Synchronization: A Simulation Study. In: 5th International Conference LDIC, 2016 Bremen, Germany, Springer International Publishing, pp. 29-40, 2016.
  • 7.Tao F., Cheng J., Cheng Y., Gu S., Zheng T. and Yang H. Robotics and Computer-Integrated Manufacturing 45, 2017, 34–46.
  • 8.B. Costa, L.S. Dias, J.A. Oliveira, and G. Pereira: In: IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth, Estori, 2008.
  • 9.Dias L.S., Vik P., Oliveira J.A. and Pereira G. Simulation in the design of an internal logistic system-Milk run delivering with Kanban control. In: 10th International Industrial Simulation Conference 2012, ISC 2012, EUROSIS, Brno, 159–66, 2012.
  • 10.Ficzere P., Ultmann Z. and Torok A. Time–space analysis of transport system using different mapping methods. Transport 3 (29), 2014, 278-284.
  • 11.Bartošíková R., Bilíková J., Strohmandl J., Šefčík V. and Taraba P. Modelling of decision-making in crisis management. In: 24th International Business Information Management Association Conference - Crafting Global Competitive Economies: 2020 Vision Strategic Planning and Smart Implementation. 2014, 1479 -1483.
  • 12.Molnár V. and Pačutová K. Assessing the possibilities of reducing the transport company costs. In: 3rd International Conference on Traffic and Transport Engineering, ICTTE 2016. 570-575, 2016.
  • 13.Molnár V. Integrated transport system - the main mean of transport for employees to US Steel Kosice. In: Carpathian Logistics Congress, CLC 2013. 561-566, 2013.
  • 14.Mantič M., Kuľka J., Krajňák J., Kopas M. and Schneider M. Influence of selected digitization methods on final accuracy of 3D model. In: Majerník, M., Daneshjo, N., and Bosák, M. (eds.) Production Management and Engineering Sciences. pp. 475–480. CRC Press Taylor and Francis Group A Balkema Book, 2016.
  • 15.Debski H., Koszalka G., Ferdynus M. Application of FEM in the analysis of the structure of a trailer supporting frame with variable operation parameters. Eksploatacja i Niezawodnosc – Maintenance and Reliability, 14, 2012, 107–113.
  • 16.Jachowicz T., Sikora R.: Methods of forecasting of the changes of polymeric products properties. Polimery, 2006, 3 (51), 11-18.
  • 17.Tow train logistics solution. Jungheinrich Official Website (http://www.jungheinrich.co.za/company/events/ifoy-award/ifoy-award-2015/tow-train-logistics-solution/)
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-659fc2cb-64a6-435d-b16b-a01735b506ec
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