EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS

Öz

Recently, important interconnected events experienced around the world, such as COVID-19, the blockage of the Suez Canal, and the decrease in the water level in the Panama Canal, have revealed the importance of logistics activities. This study aimed to evaluate the logistics performances of European Union (EU) candidates and member countries using Multi-Criteria Decision-Making (MCDM) methods. This study applied the six Logistic Performance Index (LPI) criteria, and it utilized a criteria-weighting method known as Weights by ENvelope and SLOpe (WENSLO) and an MCDM method called Alternative Ranking Technique based on Adaptive Standardized Intervals (ARTASI) to assess 8 EU candidates (EUc) and 27 EU members (EUm). The findings are compared with the ANGLE, CRITIC, CVM, ENTROPY, GINI, LOPCOW, MEREC, and SD methods for the WENSLO method, and the MABAC, MARCOS, WASPAS, TOPSIS, CRADIS, PIV, and CoCoSo methods are used for the ARTASI method. Finland, a Northern European high-income economy, was ranked first, and Cyprus, although it is an island country and may have logistical connections with many countries, was ranked last among EU countries. On the other hand, Türkiye, which ranks first among the EUc for the LPI by the MCDM, is in a better situation than some EUm. However, other candidates are ranked after the members. This study addresses a relevant and timely topic in the field of logistics performance. In this regard, the use of innovative methods (WENSLO and ARTASI) sets the paper apart from other studies.

Anahtar Kelimeler

• LPI
• WENSLO
• ARTASI
• MCDM
• Logistics

AB'YE ADAY VE ÜYE ÜLKELERİN LOJİSTİK PERFORMANSLARININ WENSLO VE ARTASI YÖNTEMLERİ KULLANILARAK DEĞERLENDİRİLMESİ

Öz

Son dönemde dünya genelinde yaşanan COVID-19, Süveyş Kanalı'nın tıkanması ve Panama Kanalı'ndaki su seviyesinin düşmesi gibi birbirine bağlı önemli olaylar lojistik faaliyetlerin önemini ortaya koymuştur. Bu çalışma ile Avrupa Birliği'ne (AB) üye ve aday ülkelerin lojistik performanslarının, Çok Kriterli Karar Verme (ÇKKV) yöntemleri kullanılarak değerlendirmesi amaçlanmaktadır. Bu çalışmada altı Lojistik Performans Endeksi (LPI) kriteri uygulanmış ve 8 AB adayı (EUc) ve 27 AB üyesini (EUm) değerlendirmek için Weights by ENvelope and SLOpe (WENSLO) olarak bilinen bir kriter ağırlıklandırma yöntemi ve Alternative Ranking Technique based on Adaptive Standardized Intervals (ARTASI) adı verilen bir ÇKKV yöntemi kullanılmıştır. Bulgular, WENSLO yöntemi için ANGLE, CRITIC, CVM, ENTROPY, GINI, LOPCOW, MEREC ve SD yöntemleri ile karşılaştırılırken, ARTASI yöntemi için MABAC, MARCOS, WASPAS, TOPSIS, CRADIS, PIV ve CoCoSo yöntemleri kullanılmıştır. Araştırma sonuçlarına göre Kuzey Avrupa'nın yüksek gelirli ekonomilerinden Finlandiya ilk sırada yer alırken, bir ada ülkesi olmasına ve birçok ülke ile lojistik bağlantısı bulunmasına rağmen Kıbrıs AB ülkeleri arasında son sırada yer almıştır. Öte yandan, ÇKKV yöntemine göre LPI için EUc arasında ilk sırada yer alan Türkiye, bazı EUm'lerden daha iyi durumdadır. Ancak diğer aday ülkeler, üyelerden sonra sıralanmıştır. Bu çalışma, lojistik performans alanında güncel ve önemli bir konuyu ele almaktadır. Bu bağlamda, yenilikçi yöntemlerin (WENSLO ve ARTASI) kullanılması, çalışmayı diğer çalışmalardan ayırmaktadır.

Anahtar Kelimeler

• LPI
• WENSLO
• ARTASI
• ÇKKV
• Lojistik

Kaynakça

1. Aggarwal, S., Aggarwal, G. & Bansal, M. (2024). Effect of Different MCDM Techniques and Weighting Mechanisms on Women Vulnerability Index. International Journal of Intelligent Systems and Applications in Engineering. 12(21s), 3291-3299.
2. Alnıpak, S. (2024). AHS-COCOSO Yöntemi ile APEC Ülkelerinin Lojistik Performanslarının Değerlendirilmesi. Tarsus Üniversitesi Uygulamalı Bilimler Fakültesi Dergisi. 4(1), 13-26.
3. Altıntaş, F. F. (2023). A Novel Approach to Measuring Criterion Weights In Multiple Criteria Decision Making: Cubic Effect-Based Measurement (CEBM). Nicel Bilimler Dergisi, 5(2), 151-195. https://doi.org/10.51541/nicel.1349382
4. Akbulut Acar, E., Ulutaş, A., Yürüyen, A.A., & Balalan, S. (2024). Hibrit bir ÇKKV Modeli ile G20 Ülkelerinin Lojistik Performansının Ölçülmesi, BMIJ 12(1): 1-21 https://doi.org/10.15295/bmij.v12i1.2300
5. Arman, K & Organ, A., (2023). AB’ye Üye ve Aday Ülkelerin Lojistik Performanslarının MEREC ve CoCoSo Yöntemleri ile Değerlendirilmesi. Uluslararası Ticaret ve Ekonomi Araştırmaları Dergisi, (7)2, 36-46. https://doi.org/10.30711/utead.1360959
6. Arvis, J.F., Ojala, L., Shepherd, B., Ulybina, D. & Wiederer, C. (2023). Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy-The Logistics Performance Index and Its Indicators. The World Bank.
7. Çakır, S. (2017). Measuring logistics performance of OECD countries via fuzzy linear regression. Journal of Multi‐Criteria Decision Analysis, 24, 177-186. https://doi.org/10.1002/mcda.1601
8. Çalık, A., Erdebilli, B., Özdemir, Y.S., (2023). Novel Integrated Hybrid Multi-Criteria Decision-Making Approach for Logistics Performance Index. Transportation Research Record, 2677(2), 1392-1400. https://doi.org/10.1177/03611981221113314

9. Çıray, D., Özdemir, Ü. & Mete, S. (2024). An Evaluation of the logistics Performance Index Using the ENTROPY-based ORESTE Method. Journal of Transportation and Logistics. 9(1), 68-82. https://doi.org/10.26650/JTL.2024.1437070
10. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW‐DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690.
11. Ersoy, N. (2021). Application of the PIV method in the presence of negative data: an empirical example from a real-world case. Hitit Journal of Social Sciences, 14(2), 318-337. http://doi.org/10.17218/hititsbd.974522
12. Gürler, H.E., Özçalıcı, M. & Pamucar, D. (2024). Determining criteria weights with genetic algorithms for multi-criteria decision-making methods: The case of logistics performance index rankings of European Union countries. Socio-Economic Planning Sciences, 91. 1-32. https://doi.org/10.1016/j.seps.2023.101758
13. Ha, L. D. (2023). Selection of suitable data normalization method to combine with the CRADIS method for making multi-criteria decision. Applied Engineering Letters, 8(1), 24-35. https://doi.org/10.18485/aeletters.2023.8.1.4
14. İnce, Ö., Çetiner, B., & Ecer, F. (2023). Benchmarking of logistics performances in G20 countries before and during COVID-19 periods: A MEREC and CODAS ıntegrated approach. Journal of Transportation and Logistics, 8(2), 112-147. https://doi.org/10.26650/JTL.2023.1317958
15. Isik, Ö., Aydin, Y. & Koşarolu, S. (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. http://doi.org/10.17270/J.LOG.2020.504
16. Janno, J., Mochalina, E.P., Ivankova, G.V., Labanova, O., Latonina, M., Safulina, E. & Uukkivi, A. (2021). The impact of initial data on the logistics performance index estimation: Estonian and Russian study. LogForum, 17(1), 147-156. https://doi.org/10.17270/J.LOG.2021.554
17. Kara K., Bentyn Z. & Yalçın G.C. (2022). Determining the logistics market performance of developing countries by entropy and MABAC methods. LogForum, 18(4), 421-434. https://doi.org/10.17270/J.LOG.2022.752
18. Kale, M. V. & Tilki, İ. (2024). Dünya Ülkelerinin Lojistik Performanslarının Çok Kriterli Karar Verme Yöntemi İle Değerlendirilmesi: 2023 Yılı Dünya Bankası Raporu İle Karşılaştırmalı Analizi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi. 80, 13-30. https://doi.org/10.51290/dpusbe.1387317
19. Keleş, N. (2023). Measuring performances through multiplicative functions by modifying the MEREC method: MEREC-G and MEREC-H. International Journal of Industrial Engineering and Operations Management. 5(3), 181-199. https://doi.org/10.1108/IJIEOM-12-2022-0068
20. Keleş, N. & Pekkaya, M. (2023) Evaluation of logistics centers in terms of sustainability via MCDM methods. Journal of Advances in Management Research, 20(2), 291-309. https://doi.org/10.1108/JAMR-04-2022-0087
21. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
22. Kizielewicz, B., Bączkiewicz, A., Shekhovtsov, A., Wątróbski, J. & Sałabun, W. (2021). Towards the RES development: Multi-criteria assessment of energy storage devices. In 2021 International Conference on Decision Aid Sciences and Application (DASA) (pp. 766-771). IEEE. https://doi.org/10.1109/DASA53625.2021.9682220
23. Kizielewicz, B., Shekhovtsov, A. & Sałabun, W. (2023). Pymcdm-The universal library for solving multi-criteria decision-making problems. SoftwareX, 22, 101368. https://doi.org/10.1016/j.softx.2023.101368
24. Manavgat, G., Demirci, A., Korkmaz, O., Koluman, A. (2023) Global scale integrated logistics performance analysis and its spillover effect. LogForum, 19(2), 245-262. https://doi.org/10.17270/J.LOG.2023.826
25. Marti, L., Martín, J.C. & Puertas, R. (2017). A Dea-Logistics Performance Index. Journal of Applied Economics, (20)1, 169-192. https://doi.org/10.1016/S1514-0326(17)30008-9
26. Mercan, Y. & Aydın, H. (2024). Logistics Performance Index of Africa: An Indicator for Türkiye And Africa Trade Relations? Süleyman Demirel University Visionary Journal. 15(42) 553-569. https://doi.org/10.21076/vizyoner.1409760
27. Mercangoz, B.A., Yildirim, B. & Yildirim, S.K. (2020). Time Period Based COPRAS-G Method: Application on the Logistics Performance Index. LogForum 16(2), 239-250. https://doi.org/10.17270/J.LOG.2020.432
28. Mešić, A., Miškić, S., Stević, Ž. & Mastilo Z. (2022). Hybrid MCDM solutions for evaluation of the logistics performance index of the Western Balkan countries. Economics, 10(1), 13-34. DOI: https://doi.org/10.2478/eoik-2022-0004
29. Miškić, S., Stević, Ž., Tadić, S., Alkhayyat, A. & Krstić, M. (2023). Assessment of the LPI of the EU countries using MCDM model with an emphasis on the importance of criteria. World Review of Intermodal Transportation Research, 11(3), 258-279. https://doi.org/10.1504/WRITR.2023.132501
30. Nguyen, P. H., Tsai, J. F., Nguyen, V. T., Vu, D. D., & Dao, T. K. (2020). A decision support model for financial performance evaluation of listed companies in the Vietnamese retailing industry. The Journal of Asian Finance, Economics, and Business, 7(12), 1005-1015.
31. Özekenci, E. K. (2023). Assessing the logistics market performance of developing countries by SWARA-CRITIC based CoCoSo methods. LogForum, 19(3), 375-394. http://doi.org/10.17270/J.LOG.2023.857
32. Pala, O. (2023). MEREC-Corr ve Saw temelli lojistik performans değerlendirme. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(25), 117-135.
33. Pamucar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
34. Pamucar, D., Ecer, F., Gligorić, Z., Gligorić, M. & Deveci, M. (2023). A Novel WENSLO and ALWAS Multicriteria Methodology and Its Application to Green Growth Performance Evaluation. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2023.3321697
35. Pamucar, D., Simic, V., Görçün, Ö.F. & Küçükönder, H. (2024). Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals. Expert Systems with Applications, 239, 122312. https://doi.org/10.1016/j.eswa.2023.122312
36. Rezaei, J., van, Roekel, W.S. & Tavasszy L. (2018). Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. https://doi.org/10.1016/j.tranpol.2018.05.007
37. Sałabun, W. & Urbaniak, K. (2020). A new coefficient of rankings similarity in decision-making problems. In Computational Science–ICCS 2020: 20th International Conference, Amsterdam, The Netherlands, June 3–5, 2020, Proceedings, Part II 20 (pp. 632-645). Springer International Publishing. https://doi.org/10.1007/978-3-030-50417-5_47.
38. Senir, G. (2021). Comparison of domestic logistics performances of Turkey nd European Union countrıes in 2018 with an integrated model. LogForum 17(2), 193-204, http://doi.org/10.17270/J.LOG.2021.576
39. Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert systems with applications, 38(10), 12160-12167.
40. Shuai, D., Zongzhun, Z., Yongji, W., & Lei, L. (2012, May). A new angular method to determine the objective weights. In 2012 24th Chinese Control and Decision Conference (CCDC) (pp. 3889-3892). IEEE.
41. Stevic, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to Compromise solution (MARCOS). Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
42. Türkoğlu, M. & Duran, G., (2023), Çok Kriterli Karar Verme Yöntemleri ile Bölgesel Kapsamlı Ekonomik Ortaklık (RCEP) Ülkelerinin Lojistik Performanslarının Değerlendirilmesi, Ekonomi Bilimleri Dergisi, 15(1): 45-69., https://doi.org/10.55827/ebd.1247297
43. Ulutaş, A. & Karaköy Ç. (2019). An analysis of the logistics performance index of EU countries with an integrated MCDM model. Economics and Business Review, 5(4), 49-69. DOI:https://doi.org/10.18559/ebr.2019.4.3
44. Wang, T. C., ve Lee, H. D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert systems with applications, 36(5), 8980-8985.
45. WTO, (n.d.). Merchandise Trade Values. Retrieved October 19, 2024 from https://stats.wto.org/
46. Yazdani, M., Zarate, P., Zavadskas, E. K., & Turskis, Z. (2018). A Combined Compromise Solution (COCOSO) method for multi-criteria decision-making problems. Management Decision, 57(9), 2501–2519. https://doi.org/10.1108/MD-05-2017-0458
47. 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. https://doi.org/10.1007/s40822-019-00131-3
48. Yu, M.M. & Rakshit I. (2023). An alternative assessment approach to global logistics performance evaluation: Common weight H‐DEA approach. International Transactions in Operational Research. 1-24. https://doi.org/10.1111/itor.13360
49. Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 3–6. https://doi.org/10.5755/j01.eee.122.6.1810