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Tytuł artykułu

Solving transport logistics problems in a virtual enterprise through artificial intelligence methods

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper offers a solution to the problem of material flow allocation within a virtual enterprise by using artificial intelligence methods. The research is based on the use of fuzzy relations when planning for optimal transportation modes to deliver components for manufactured products. The Fuzzy Logic Toolbox is used to determine the optimal route for transportation of components for manufactured products. The methods offered have been exemplified in the present research. The authors have built a simulation model for component transportation and delivery for manufactured products using the Simulink graphical environment for building models.
Czasopismo
Rocznik
Strony
31--42
Opis fizyczny
Bibliogr. 22 poz.
Twórcy
autor
  • National Aerospace University “Kharkiv Aviation Institute” 17, Chkalova str., Kharkiv, Ukraine
autor
  • National Aerospace University “Kharkiv Aviation Institute” 17, Chkalova str., Kharkiv, Ukraine
autor
  • National Aerospace University “Kharkiv Aviation Institute” 17, Chkalova str., Kharkiv, Ukraine
  • National Aerospace University “Kharkiv Aviation Institute” 17, Chkalova str., Kharkiv, Ukraine
autor
  • National Aerospace University “Kharkiv Aviation Institute” 17, Chkalova str., Kharkiv, Ukraine
Bibliografia
  • 1. Martinez, M.T. & Fouletier, P. & Park, K.H. & Favrel, J. Virtual enterprise – organization, evolution and control. International journal of production economics. 2001. Vol. 74(1). P. 225-238.
  • 2. Chen, D. A methodology for developing service in virtual manufacturing environment. Annual Reviews in Control. 2015. Vol. 39. P. 102-117.
  • 3. Esposito, E. & Evangelista, P. Investigating virtual enterprise models: literature review and empirical findings. International Journal of Production Economics. 2014. Vol. 148. P. 145-157.
  • 4. Camarinha-Matos, L.M. & Afsarmanesh, H. & Ollus, M. Methods and tools for Collaborative Networked Organizations. Springer. 2008. 229 p.
  • 5. Barnett, W. & Presley, A. & Johnson, M. & Liles, D.H. An architecture for the virtual enterprise. Humans, Information and Technology. 1994. Vol. 1. P. 506-511.
  • 6. Hummels, D. Transportation costs and international trade in the second era of globalization. The Journal of Economic Perspectives. 2007. Vol. 21(3). P. 131-154.
  • 7. Liu, J.J. Supply chain management and transport logistics. Routledge. 2011. 530 p.
  • 8. Taniguchi, E. & Thompson, R.G. City Logistics Network Modelling and Intelligent Transport Systems. 2011. 264 p.
  • 9. Nazemi, A. & Omidi, F. An efficient dynamic model for solving the shortest path problem. Transportation Research Part C: Emerging Technologies. 2013. Vol. 26. P. 1-19.
  • 10. Lai, K.-H. & Cheng, T.C.E. Supply chain performance in transport logistics: an assessment by service providers. International Journal of Logistics: Research and Applications. 2003. Vol. 6(3). P. 151-164.
  • 11. Qu, Fengzhong & Fei-Yue Wang & Liuqing Yang. Intelligent transportation spaces: vehicles, traffic, communications, and beyond. IEEE Communications Magazine. 2010. Vol. 48(11). P. 136-142.
  • 12. Dimitrakopoulos, G. & Panagiotis D. Intelligent transportation systems. IEEE Vehicular Technology Magazine. Vol. 5(1). P. 77-84.
  • 13. Murat, Y.S. & Uludag, N. Route Choice Modelling in Urban Transportation Networks using Fuzzy Logic and Logistic Regression Methods. Journal of Scientific and Industrial Research (JSIR). 2008. Vol. 67. P. 19-27.
  • 14. Murat, Y.S. Comparison of Fuzzy Logic and Artificial Neural Networks Approaches in Vehicle Delay Modeling. Transportation Research Part C: Emerging Technologies. 2006. Vol. 14(5). P. 316-334.
  • 15. Yager, R.R. & Lotfi A.Z. (eds.). An introduction to fuzzy logic applications in intelligent systems. Vol. 165. Springer Science & Business Media. 2012. 356 p.
  • 16. Hunt, V.D. Artificial intelligence & expert systems sourcebook. Springer Science & Business Media. 2012. 315 p.
  • 17. Russell, S. & Norvig, P. Artificial Intelligence. A Modern Approach. 2-nd edition. Prentice Hall. 2003. 932 p.
  • 18. Ronald, J. & Brachman, Hector, J. Levesque & Raymond, Reiter. Knowledge Representation. MIT Press. Cambridge. Massachusetts. USA. 1992. 408 p.
  • 19. Poole, D.L. & Mackworth, A.K. Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press. 2010. 662 p.
  • 20. James, B. Dabney & Thomas, L. Harman. Mastering simulink. Pearson/Prentice Hall. 2004. 376 p.
  • 21. Karris, Steven T. Introduction to Simulink with engineering applications. Orchard Publications, 2006. 584 p.
  • 22. Ismail, H. Altas & Adel, M. Sharaf. A generalized direct approach for designing fuzzy logic controllers in Matlab/Simulink GUI environment. International Journal of Information Technology and Intelligent Computing. 2007. Vol. 1(4). P. 1-27
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7ea2bb4a-8f98-4bbb-90fa-2809e98efc5f
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