International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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:: International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

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ISSN 2228-9860
eISSN 1906-9642
CODEN: ITJEA8


FEATURE PEER-REVIEWED ARTICLE

Vol.12(7) (2021)

References

  1. Ali, M., Tarbulsi, M., & Majeed, A. (2019). Challenges in Implementing Transportation Tracking System in Saudi Arabia. Proceedings of the 2019 IEEE IEEM, (pp. 973-977).
  2. Arora, P., Deepali, D., & Varshney, S. (2016 ). Analysis of K-Means and K-Medoids Algorithm For Big Data. Procedia Computer Science, 78, 507-512.
  3. Arvis, J.-F., Ojala, L., Wiederer, C., Shepherd, B., A., R., & Dairabayeva, K. (2018). Connecting to compete - Trade logistics in the global economy: The logistics performance index and its indicators. Washington, DC.: World Bank.
  4. Benotmane, Z., Belalem, G., & Neki, A. (2018). Towards a cloud computing in the service of green logistics. International Journal of Logistics Systems and Management, 29(1), 37-61. doi: 10.1504/ijlsm.2018.088579
  5. Beysenbaev, R., & Dus, Y. (2020). , Proposals for improving the Logistics Performance Index. Asian journal of Shipping and Logistics, pp.34-42.
  6. Bindra, K., & Mishra, A. (2017). A detailed study of clustering algorithms. 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 371-376, Noida.
  7. Eyefortransport. (2019). The Supply Chain Hot Trends 2019 report.
  8. Gao, J., Ma, J., Zhang, X., & Lu, D. (2012). Cloud computing based logistics resource dynamic integration and collaboration. Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), (pp. 939-943).
  9. Isik O., Aydin Y., Kosaroglu S.M. (2020). The Assessment of The Logistics Performance Index of CEE Countries with the New Combination of SV and MABAC Methods. LogForum, 16(4), 549-559, DOI: 10.17270/J.LOG.2020.504.
  10. Ittmann, H. (2018). Logistics performance in South Africa). Journal of Transport and Supply Chain Management, pp. 1-3.
  11. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognit. Letters, 31(8), pp. 651-666.
  12. Janno J., Mochalina E.P., Ivankova G.V., Labanova O., Latonina M., Safulina E., Uukkivi A., 2020. The Impact of Initial Data on the Logistics Performance Index Estimation: Estonian and Russian Study. LogForum, 17(1), 147-156, DOI: 10.17270/J.LOG.2021.554
  13. Kisa A.C.G., Aycin E., 2019. Evaluation of the Logistics Performance of OECD Countries with EDAS Method Based on SWARA. Cankiri Karatekin University Faculty of Economics and Administrative Sciences Journal, 9(1), 301-325, DOI: 10.18074/ckuiibfd.500320.
  14. Li, M., & Wu, X. (2012). Application and research on Information delivering in logistics information portal under a cloud computing enviroment. International Conference on Systems and Informatics (ICSAI2012). DOI: 10.1109/icsai.2012.6223138
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  16. Nanda, S. R., Mahanty, B., & Tiwari, M. K. (2010). Clustering Indian stock market data for portfolio management. Expert Systems with Applications, 12, 8793-8798. doi:10.1016/j.eswa.2010.06.026.
  17. Orhan M. (2019). Comparison of the Logistics Performance Between Turkey and European Union Member Countries with ENTROPY Weighted EDAS Method. European Journal of Science and Technology, (17), 1222-1238, DOI: 10.31590/ejosat.657693.
  18. Ouyang, L. Y.-Y. (2019). A Blockchain-based Framework for Collaborative Production in Distributed and Social Manufacturing. 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). doi: 10.1109/soli.
  19. Ouyang, L., Yuan, Y., & & Wang, F.-Y. (2019). A Blockchain-based Framework for Collaborative Production in Distributed and Social Manufacturing. 2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI).
  20. Oyelade, J., Isewon, I., Oladipupo, O., Emebo, O., Omogbadegun, Z., Aromolaran, O., & Olawole, O. (2019). Data Clustering: Algorithms and Its Applications. 19th International Conference on Computational Science and Its Applications (ICCSA). (pp. 71-81). IEEE.
  21. Senir G., 2021. Comparison of domestic logistics performances of Turkey And European Union countries in 2018 with an integrated model. LogForum, 17(2), 193-204, DOI: 10.17270/J.LOG.2021.576
  22. Subramanian, N., Abdulrahman, M. D., & Zhou, X. (2014). Integration of logistics and cloud computing service providers: Cost and green benefits in the Chinese context. Transportation Research Part E: Logistics and Transportation Review, 70, pp. 86-98.
  23. Ulutas, A., & Karakoy, C. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(19), 49-69.
  24. Wang, J., Lim, M. K., Zhan, Y., & Wang, X. (2020). An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Logistics and Transportation Review, 135, pp. 1-23.
  25. WEF, W. E. (2017). Supply chain and transport - Explore the latest strategic trends, research and analysis. https://toplink.weforum.org/knowledge/insight/a1Gb0000000pTDoEAM/explore/summary Retrieved Jan 2021.
  26. Yildirim B.F., Mercangoz B.A., 2020. Evaluating the Logistics Performance of OECD Countries by Using Fuzzy AHP and ARAS-G. Eurasian Economic Review, 10(1), 27-45, DOI: 10.1007/s40822-019-00131-3.
  27. Zhang, C., Xu, M., & Qiu, C. (2017). Complex-Network-Based Supply Chain Relationships Inference in Cloud Logistics Services. 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). DOI: 10.1109/cse-euc.2017.120


Other issues:
Vol.12(6)(2021)
Vol.12(5)(2021)
Vol.12(4)(2021)
Vol.12(3)(2021)
Vol.12(2)(2021)
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