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

Yıl 2025, Sayı: 68, 43 - 66, 12.05.2025

Nuh Keleş , Ata Kahveci

https://doi.org/10.30794/pausbed.1594714

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

  • 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.
  • Alnıpak, S. (2024). AHS-COCOSO Yöntemi ile APEC Ülkelerinin Lojistik Performanslarının Değerlendirilmesi. Tarsus Üniversitesi Uygulamalı Bilimler Fakültesi Dergisi. 4(1), 13-26.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • Pala, O. (2023). MEREC-Corr ve Saw temelli lojistik performans değerlendirme. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(25), 117-135.
  • 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
  • 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
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EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS

Yıl 2025, Sayı: 68, 43 - 66, 12.05.2025

Nuh Keleş , Ata Kahveci

https://doi.org/10.30794/pausbed.1594714

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

Kaynakça

  • 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.
  • Alnıpak, S. (2024). AHS-COCOSO Yöntemi ile APEC Ülkelerinin Lojistik Performanslarının Değerlendirilmesi. Tarsus Üniversitesi Uygulamalı Bilimler Fakültesi Dergisi. 4(1), 13-26.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • Pala, O. (2023). MEREC-Corr ve Saw temelli lojistik performans değerlendirme. Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(25), 117-135.
  • 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
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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.
  • 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.
  • 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
  • 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
  • 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
  • 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.
  • WTO, (n.d.). Merchandise Trade Values. Retrieved October 19, 2024 from https://stats.wto.org/
  • 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
  • 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
  • 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
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Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Lojistik
Bölüm Araştırma Makalesi
Yazarlar

Nuh Keleş 0000-0001-6768-728X

Ata Kahveci 0000-0002-2010-614X

Erken Görünüm Tarihi 2 Mayıs 2025
Yayımlanma Tarihi 12 Mayıs 2025
Gönderilme Tarihi 2 Aralık 2024
Kabul Tarihi 5 Şubat 2025
Yayımlandığı Sayı Yıl 2025 Sayı: 68

Kaynak Göster

APA Keleş, N., & Kahveci, A. (2025). EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(68), 43-66. https://doi.org/10.30794/pausbed.1594714
AMA Keleş N, Kahveci A. EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS. PAUSBED. Mayıs 2025;(68):43-66. doi:10.30794/pausbed.1594714
Chicago Keleş, Nuh, ve Ata Kahveci. “EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 68 (Mayıs 2025): 43-66. https://doi.org/10.30794/pausbed.1594714.
EndNote Keleş N, Kahveci A (01 Mayıs 2025) EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 68 43–66.
IEEE N. Keleş ve A. Kahveci, “EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS”, PAUSBED, sy. 68, ss. 43–66, Mayıs 2025, doi: 10.30794/pausbed.1594714.
ISNAD Keleş, Nuh - Kahveci, Ata. “EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 68 (Mayıs 2025), 43-66. https://doi.org/10.30794/pausbed.1594714.
JAMA Keleş N, Kahveci A. EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS. PAUSBED. 2025;:43–66.
MLA Keleş, Nuh ve Ata Kahveci. “EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 68, 2025, ss. 43-66, doi:10.30794/pausbed.1594714.
Vancouver Keleş N, Kahveci A. EVALUATING THE LOGISTICS PERFORMANCE OF THE EU CANDIDATE AND MEMBER COUNTRIES USING THE WENSLO AND ARTASI METHODS. PAUSBED. 2025(68):43-66.
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