Evaluation of the Performance of Logistics Companies by the ENTROPY-VICOR Method During the COVID-19 Pandemıc Process

Öz

The aim of this study is to discuss the performance evaluation of logistics companies that are in the top ten every year in the evaluation list of the 2020-2022 European Conference of Ministers of Transport (UBAK) during the COVID-19 pandemic. For this purpose, a Multi-Criteria Decision Making (MCDM) approach combining ENTROPI and VIKOR methods was used. In the first stage of the study, the objective weights of the criteria were calculated with Entropy. In the second stage of the study, the performance of logistics companies was determined and ranked according to the VIKOR method. According to the findings obtained from the entropy weighting method, the two most important performance criteria are the 3rd Country Expedition Per Document (BB3ÜS) and the Warning Penalties from UBAK (UVIC), respectively. According to the results obtained with the Entrop-VIKOR method, it has been determined that the F7 company for the years 2020 and 2021 and the F5 company for the year 2022 are the most successful companies in terms of the performance criteria used. The findings of this study are of great importance for logistics companies that have to use their resources effectively and efficiently under intense competition conditions in order to maintain and increase their existing market shares, and for decision-makers working to create a standard logistics system.

Anahtar Kelimeler

• Entropy
• VIKOR
• Road Transport
• Covid 19

COVİD-19 Pandemisi Sürecinde Lojistik Firmalarının Performansının ENTROPİ-VİKOR Yöntemi ile Değerlendirilmesi

Öz

Bu çalışmada amaç COVID-19 pandemesi sürecinde 2020-2022 Ulaştırma Bakanları Avrupa Konferansı (UBAK) değerlendirme listesinde her yıl ilk onda yer alan lojistik firmalarının performans değerlendirmesini ele almaktır. Bu amaçla ENTROPİ ve VIKOR yöntemlerini birleştiren bir Çok Kriterli Karar Verme (ÇKKV) yaklaşımı kullanılmıştır. Çalışmanın ilk aşamasında kriterlere ilişkin objektif ağırlıklar Entropi ile hesaplanmıştır. Çalışmanın ikinci aşamasında ise lojistik firmaların performansı VİKOR yöntemine göre belirlenip sıralanmıştır. Entropi ağırlıklandırma yönteminden elde edilen bulgulara göre en önemli iki performans kriteri sırasıyla Belge Başına 3.Ülke Seferi (BB3ÜS) ve UBAK'tan Verilen İhtar Cezalarıdır (UVİC). Entrop-VİKOR yöntemi ile ulaşılan sonuçlara göre, kullanılan performans kriterleri açısından 2020 ve 2021 yılları için F7 ve 2022 yılı için de F5 firması en başarılı firmalar olduğu belirlenmiştir. Bu çalışmanın bulguları, mevcut pazar paylarını korumak ve artırmak için yoğun rekabet koşulları altında kaynaklarını etkin ve verimli şekilde kullanmak zorunda olan lojistik firmaları ve standart bir lojistik sistemi oluşturmak için çalışan karar verici taraflar acısından büyük önem taşımaktadır.

Anahtar Kelimeler

• Entropi
• VIKOR
• Karayolu Taşımacılığı
• COVID-19

Kaynakça

1. Adhikari, A., Bhattacharyya, S., Basu, S., & Bhattacharya, R. (2021). Evaluating the performance of primary schools in India: evidence from West Bengal. International Journal of Productivity and Performance Management, 71(7), 2630-2658.
2. Aktaş, N, Demirel, N., (2021). A hybrid framework for evaluating corporate sustainability using multi-criteria decision making. Environment, Development and Sustainability, 23(10), 15591-15618.
3. Aydıner, E., & Şener, H. Y. (2021). Bir işletmenin lojistik hizmet sağlayıcılarının farklı kıtalar ve pandeminin etkisi açısından değerlendirilmesi. Afyon Kocatepe Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 23(1), 53-73.
4. Basuki, A. (2016). Sustainable strategies selection in SMEs using MCDM approach. In MATEC Web of Conferences, 58, 02007.
5. Borjalilu, N., Sazvar, Z., & Nayeri, S. (2021). An integrated method for airline company supplier selection based on the entropy and vikor methods: a real case study. International Journal of Aviation, Aeronautics and Aerospace, 8(4), 1.
6. Chen, Y. H. (2020). Applying Linguistic VIKOR and Entropy to Evaluate Competitive Ability of Medical Tourism in Taiwan. In 2020 IEEE 2nd Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), 190-192.
7. Fei, L., Deng, Y., & Hu, Y. (2019). DS-VIKOR: A new multi-criteria decision-making method for supplier selection. International Journal of Fuzzy Systems, 21(1), 157-175.
8. Gök-Kısa, A. C., ve Perçin, S. (2018). Bütünleşik Entropi Ağırlık-VIKOR Yöntemi İle Bilişim Teknolojisi Sektöründe Performans Ölçümü. Ekonomik ve Sosyal Araştırmalar Dergisi, 14(1), 1-13.

9. Hsu, L. C. (2015). Using a decision-making process to evaluate efficiency and operating performance for listed semiconductor companies. Technological and Economic Development of Economy, 21(2), 301-331.
10. Jovanovic, J., Shah, H., Vujovic, A., & Krivokapic, Z. (2014). Application of MCDM methods in evaluation of environmental impacts. International Journal for Quality Research, 8(4).
11. Lee, H. C., & Chang, C. T. (2018). Comparative analysis of MCDM methods for ranking renewable energy sources in Taiwan. Renewable and Sustainable Energy Reviews, 92, 883-896.
12. Lotfi, F. H., & Fallahnejad, R. (2010). Imprecise Shannon’s entropy and multi attribute decision making. Entropy, 12(1), 53-62.
13. Mijalkovski, S., Despodov, Z., Mirakovski, D., Adjiski, V., Doneva, N., & Mijalkovska, D. (2021). Mining method selection for underground mining with the application of VIKOR method. Podzemni Radovi, (39), 11-22.
14. Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of civil engineering, Belgrade, 2(1), 5-21.
15. Opricovic, S. & Tzeng, G.H. (2004). Compromise solution by MCDM methods: A Comparative Analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156: 445–455.
16. Opricovic, S., & Tzeng, G. H. (2002). Multicriteria planning of post‐earthquake sustainable reconstruction. Computer‐Aided Civil and Infrastructure Engineering, 17(3), 211-220.
17. Radhika, S., Kumar, D. S., & Swapna, D. (2013). VIKOR method for multi criteria decision making in academic staff selection. Journal of Production Research and Management, 3(2), 30-35.
18. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
19. Siew, L. W., Fai, L. K., & Hoe, L. W. (2021). Performance evaluation of construction companies in Malaysia with Entropy-VIKOR model. Engineering Journal, 25(1), 297-305.
20. Sindhwani, R., Lata Singh, P., Kaushik, V., Sharma, S., Kumar Phanden, R., & Kumar Prajapati, D. (2020). Ranking of factors for integrated lean, green and agile manufacturing for indian manufacturing SMEs. In Advances in
21. Stanković, J. J., Popović, Ž., & Marjanović, I. (2019). Assessing Smartness and Urban Development of the European Cities: An Integrated Approach of Entropy and VIKOR. In International Conference on Multiple Criteria Decision Making, 69-97.
22. Switala, M., & Lukasiewicz, A. (2021). Road freight transport companies facing the COVID-19 pandemic. Gospodarka Materiałowa i Logistyka.
23. T.C. Sanayi ve Kalkınma Bakanlığı, (2021). COVID-19 Krizine yanıt ve dayanıklılık projesi; Lojistik sektörü analiz raporu ve kılavuzu, Sanayi ve Teknoloji Bakanlığı Kalkınma Ajansları Genel Müdürlüğü, 2021, Ankara.
24. Teimouri, H. B., Gharibi, J., Hossein Zadeh, A., & Pooya, A. (2021). An integrated entropy/VIKOR model for customer clustering in targeted marketing model design (case study: IoT technology services companies). Advances in Mathematical Finance and Applications, 6(4), 671-693.
25. UBAK, (2022). UBAK 2020-2022 Değerlendirme kriterleri, (Ulaştırma ve Altyapı Bakanlığı) https://uhdgm.uab.gov.tr/, Erişim Tarihi: 10.07.2022.
26. Wang, W., & Lin, W. (2018). Multi-criteria decision making model for evaluation of the growth of environmental protection enterprises. Ecology, 27(106), 1887-1894.
27. Wang, W., Lin, W., Bao, Z., Dai, X., & Lin, Q. (2022). Study on the influence of COVID-19 on the growth of China’s small and medium-sized construction enterprises. PloS one, 17(6), e0266315.
28. Xie, L., Ni, H., Yang, H., & Zhang, J. (2020). A key business node identification model for internet of things security. Security and Communication Networks.
29. Zeleny, M., 1982. Multiple criteria decision making. McGraw-Hill, New York.
30. Zhang, J., Li, L., Zhang, J., Chen, L., & Chen, G. (2021). Private-label sustainable supplier selection using a fuzzy entropy-VIKOR-based approach. Complex & Intelligent Systems, 1-18.
31. Zheng, Q., Chen, K., Kong, D., Liang, J., & Wu, J. (2021). A Selection model of strategic emerging ındustries in fujian based on entropy-VIKOR algorithm. Discrete Dynamics in Nature and Society, 2021.