Open Access
MATEC Web Conf.
Volume 81, 2016
2016 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016)
Article Number 06001
Number of page(s) 5
Section Supply Chains
Published online 25 October 2016
  1. Ko M, Tiwari A, Mehnen J, “A review of soft computing applications in supply chain management” Applied Soft Computing, 10, (2010), pp. 661–674 [CrossRef] [Google Scholar]
  2. Botzheim J, Földesi P, “Fuzzy neural network with novel computation of fuzzy exponent in the sigmoid functions.” In: Proceedings of the 8th International Symposium on Management Engineering, ISME 2011, Taipei, Taiwan, pp. 285–291 [Google Scholar]
  3. Botzheim J, Cabrita C, Kóczy LT, Ruano AE, “Fuzzy rule extraction by bacterial memetic algorithms.” In: Proceedings of the 11th World Congress of International Fuzzy Systems Association, IFSA 2005, Beijing, China, pp. 1563–1568 [Google Scholar]
  4. Bozarth CC, Warsing DP, Flynn BB, Flynn EJ, “The impact of supply chain complexity on manufacturing plant performance.” Journal of Operations Management 27(1) (2009), pp. 78–93 [CrossRef] [Google Scholar]
  5. Dubois A, Hulthén K, Pedersen AC “Supply chains and interdependence: a theoretical analysis.” Journal of Purchasing and Supply Management 10(1) (2004), pp. 3–9 [CrossRef] [Google Scholar]
  6. Gál L, Botzheim J, Kóczy LT, Ruano AE, “Applying bacterial memetic algorithm for training feedforward and fuzzy flip-flop based neural networks.” In: Proceedings of the 2009 IFSA World Congress and 2009 EUSFLAT Conference, IFSA-EUSFLAT 2009, Lisbon, Portugal, pp. 1833–1838 [Google Scholar]
  7. Hecht-Nielsen R, Neurocomputing. Addison-Wesley (1990) [Google Scholar]
  8. Moscato P, “On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms.” Tech. Rep. Caltech Concurrent Computation Program, Report. 826, (1989) California Institute of Technology, Pasadena, California, USA [Google Scholar]
  9. Nawa NE, Furuhashi T: “Fuzzy system parameters discovery by bacterial evolutionary algorithm.” IEEE Transactions on Fuzzy Systems 7(5) (1999), pp. 608–616 [CrossRef] [Google Scholar]
  10. Németh P, “Flexibility in supply chains” Acta Technica Jaurinensis Series Logistica, 1(2) (2008) pp.371–379. [Google Scholar]
  11. Németh P, “Ellátási láncok hatékony irányítása multi kritériumos teljesítmény méréssel” (in Hungarian). Ph.D. thesis, 2009, Széchenyi István University, Győr, Hungary [Google Scholar]
  12. Németh P, Földesi P, Botzheim J, “Enhancing warehouse performance at a global company” In: Proceedings of Knowledge Globalization Conference, Boston, Massachusetts, (October 2011), pp. 7–19. [Google Scholar]
  13. Németh P, Földesi P, Csík Á, “The concept of logistic space in the modelling of supply chain performance” In: Proceedings of the 22nd Annual Production and Operations Management Society Conference, Reno, Nevada, (May 2011) [Google Scholar]
  14. Zurada JM, Introduction to Artificial Neural Systems, West Publishing Co., St. Paul (1992) [Google Scholar]
  15. Bhagwat R, Sharma MK, “Performance measurement of supply chain management: A balanced scorecard approach”, Computers & Industrial Engineering 53(1) (2007) pp. 43–62 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.