Ulaşım Altyapı Performansının Çok Kriterli Karar Verme Yöntemleriyle Değerlendirilmesi: Seçilmiş Ülkeler Üzerine Bir Analiz

Year 2024, Volume: 9 Issue: 24, 238 - 256

Hasan Emin Gürler

https://doi.org/10.25204/iktisad.1454785

Abstract

Bu çalışmada, ülkelerin ulaşım altyapı performanslarına ilişkin objektif bir performans değerlendirme modeli önermek amaçlanmıştır. Bu kapsamda, 2023 yılı LPI altyapı skoruna göre ilk 10’da yer alan ülkeler, entegre Entropi-EDAS ve Entropi-WASPAS yaklaşımları kullanılarak ulaşım altyapı performanslarına göre değerlendirilmiştir. Veri seti, Dünya Ekonomik Forumu, UNCTAD ve Global Firepower gibi kamuya açık kaynaklardan elde edilmiştir. Ülkeler, tümü fayda odaklı olan toplam 8 kritere göre değerlendirilmiştir. Kriter ağırlıkları, Entropi yöntemiyle belirlenirken ülkeler, EDAS ve WASPAS yöntemlerine göre sıralanmıştır. Sonuçlar, Filo Büyüklüğü'nün en önemli kriter olduğunu, Hava Taşımacılığı Hizmetlerinin Verimliliği'nin ise en az önemli kriter olduğunu göstermektedir. EDAS ve WASPAS yöntemlerine göre, Japonya, ulaşım altyapısı performansı açısından birinci sırada yer alırken, İsviçre son sırada yer almaktadır. Çalışmada önerilen performans değerlendirme modelinin oldukça güvenilir ve tutarlı sonuçlar sunduğu anlaşılmaktadır. Önerilen değerlendirme modelinin, kriter ağırlıklarını objektif bir şekilde belirleyebilme ve aynı anda birden fazla çok kriterli karar verme tekniğini birlikte kullanabilme bakımından oldukça avantajlı olduğu düşünülmektedir.

Keywords

Ulaşım Altyapısı, Lojistik Performans Endeksi, Entropi, Edas, Waspas

Evaluation of Transportation Infrastructure Performance with Multi-Criteria Decision Making Methods: An Analysis on Selected Countries

Abstract

This study aims to propose an objective performance assessment model for countries' transportation infrastructure performance. In this context, countries ranked in the top 10 by the LPI infrastructure score for 2023 were evaluated by their transportation infrastructure performance using the integrated Entropy-EDAS and Entropy-WASPAS. The dataset was obtained from publicly available sources such as the World Economic Forum, UNCTAD and Global Firepower. Countries are evaluated by a total of 8 criteria, all of which are utility-oriented. Criteria weights are determined by Entropy method and countries are ranked by EDAS and WASPAS methods. The results show that Fleet Size is the most important criterion, while Efficiency of Air Transportation Services is the least important criterion. According to the EDAS and WASPAS, Japan ranks first in terms of transportation infrastructure performance, while Switzerland ranks last. It is understood that the performance evaluation model proposed in the study provides very reliable and consistent results. The proposed evaluation model is considered to be very advantageous in terms of determining the criteria weights objectively and using more than one multi-criteria decision-making technique at the same time.

Keywords

Transportation Infrastructure, Logistics Performance Index, Entropy, Edas, Waspas

References

• Ayçin, E. ve Orçun, Ç. (2019). Mevduat bankalarının performanslarının entropi ve mairca yöntemleri ile değerlendirilmesi. Balıkesir Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22(42), 175-194. https://doi.org/10.31795/baunsobed.657002
• Blumenfeld, M., Wemakor, W., Azzouz, L. ve Roberts, C. (2019). Developing a new technical strategy for rail infrastructure in low-income countries in sub-Saharan Africa and south Asia. Sustainability (Switzerland), 11(16): 4319. https://doi.org/10.3390/su11164319
• Bouraima, M. B., Saha, A., Stević, Ž., Antucheviciene, J., Qiu, Y. ve Marton, P. (2023). Assessment actions for improving railway sector performance using intuitionistic fuzzy-rough multi-criteria decision-making model. Applied Soft Computing, 148: 110900. https://doi.org/10.1016/j.asoc.2023.110900
• Champagne, M. P. ve Dubé, J. (2023). The impact of transport infrastructure on firms’ location decision: A meta-analysis based on a systematic literature review. Transport Policy, 131, 139-155. https://doi.org/10.1016/j.tranpol.2022.11.015
• De Bartolomeo, D., Renzi, E., Tamasi, G., Palermo, G. ve Di Nucci, F. (2023). The Italian risk-based approach for the development of an integrated safety management system for road infrastructures and its relations with innovative guidelines on the risk management of existing bridges. Transportation Research Procedia, 69, 886–893. https://doi.org/10.1016/j.trpro.2023.02.249
• Deveci, M., Canıtez, F. ve Gökaşar, I. (2018). Waspas and topsis based interval type-2 fuzzy mcdm method for a selection of a car sharing station. Sustainable Cities and Society, 41, 777-791. https://doi.org/10.1016/j.scs.2018.05.034
• Ecer, F. (2021). A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies. Renewable and Sustainable Energy Reviews, 143: 110916. https://doi.org/10.1016/j.rser.2021.110916
• Eghbali-Zarch, M., Tavakkoli-Moghaddam, R., Dehghan-Sanej, K. ve Kaboli, A. (2022). Prioritizing the effective strategies for construction and demolition waste management using fuzzy idocriw and waspas methods. Engineering, Construction and Architectural Management, 29(3), 1109–1138. https://doi.org/10.1108/ECAM-08-2020-0617
• George, T. B., Mokoena, R. ve Rust, F. C. (2018). A review on the current condition of rail infrastructure in South Africa. 37th Annual Southern African Transport Conference (SATC 2018), 496–507.
• Hafezalkotob, A. ve Hafezalkotob, A. (2015). Extended multimoora method based on shannon entropy weight for materials selection. Journal of Industrial Engineering International, 12(1), 1-13. https://doi.org/10.1007/s40092-015-0123-9
• Hussain, Z., Hanif, N., Shaheen, W. A. ve Nadeem, M. (2019). Empirical analysis of multiple infrastructural covariates: An application of gravity model on asian economies. Asian Economic and Financial Review, 9(3), 299-317. https://doi.org/10.18488/journal.aefr.2019.93.299.317
• Ijadi Maghsoodi, A., Abouhamzeh, G., Khalilzadeh, M. ve Zavadskas, E. K. (2018). Ranking and selecting the best performance appraisal method using the multimoora approach integrated shannon’s entropy. Frontiers of Business Research in China, 12(1). https://doi.org/10.1186/s11782-017-0022-6
• Inti, S. ve Tandon, V. (2017). Application of fuzzy preference–analytic hierarchy process logic in evaluating sustainability of transportation infrastructure requiring multicriteria decision making. Journal of Infrastructure Systems, 23(4). https://doi.org/10.1061/(ASCE)IS.1943-555X.0000373
• Kadyraliev, A., Supaeva, G., Bakas, B., Dzholdosheva, T., Dzholdoshev, N., Balova, S., Tyurina, Y. ve Krinichansky, K. (2022). Investments in transport infrastructure as a factor of stimulation of economic development. Transportation Research Procedia, 63, 1359-1369. https://doi.org/10.1016/j.trpro.2022.06.146
• Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z. ve Antucheviciene, J. (2017). Stochastic edas method for multi-criteria decision-making with normally distributed data. Journal of Intelligent & Fuzzy Systems, 33(3), 1627-1638. https://doi.org/10.3233/JIFS-17184
• Korinek, J. ve Sourdin, P. (2010). Clarifying trade costs: Maritime transport and its effect on agricultural trade. Applied Economic Perspectives and Policy, 32(3), 417-435. https://doi.org/10.1093/aepp/ppq007
• Kundakcı, N. (2019). An integrated method using macbeth and edas methods for evaluating steam boiler alternatives. Journal of Multi-Criteria Decision Analysis, 26(1-2), 27-34. https://doi.org/10.1002/mcda.1656
• Liu, A., Li, Z., Shang, W. L. ve Ochieng, W. (2023). Performance evaluation model of transportation infrastructure: perspective of covid-19. Transportation Research Part A: Policy and Practice, 170: 103605. https://doi.org/10.1016/j.tra.2023.103605
• Mao, H., Cui, G., Hussain, Z. ve Shao, L. (2024). Investigating the simultaneous impact of infrastructure and geographical factors on international trade: Evidence from Asian economies. Heliyon, 10(1): e23791. https://doi.org/10.1016/j.heliyon.2023.e23791
• Mitra, A. (2022). Selection of cotton fabrics using edas method. Journal of Natural Fibers, 19(7), 2706-2718. https://doi.org/10.1080/15440478.2020.1821289
• Nassereddine, M. ve Eskandari, H. (2017). An integrated mcdm approach to evaluate public transportation systems in Tehran. Transportation Research Part A: Policy and Practice, 106, 427-439. https://doi.org/10.1016/j.tra.2017.10.013
• Ozcalici, M. (2022). Asset allocation with multi-criteria decision making techniques. Decision Making: Applications in Management and Engineering, 5(2), 78-119. https://doi.org/10.31181/dmame0305102022o
• Pisa, N. M. (2021). Innovations to improve rail freight efficiency: Considerations for emerging economies. Journal of Contemporary Management, 18(1), 223–242. https://doi.org/10.35683/jcm20093.103
• Popova, Y. (2017). Relations between wellbeing and transport infrastructure of the country. Procedia Engineering, 178, 579-588. https://doi.org/10.1016/j.proeng.2017.01.112
• Rehman, F. U., Islam, M. M., Miao, Q. ve Metwally, A. S. M. (2023). Does transport infrastructure make south asian economies growth more inclusive? An application of a new transportation infrastructure index. Research in Transportation Business and Management, 49: 101013 https://doi.org/10.1016/j.rtbm.2023.101013
• Rezaei, J., van Roekel, W. S. ve 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
• Saidi, S., Shahbaz, M. ve Akhtar, P. (2018). The long-run relationships between transport energy consumption, transport infrastructure, and economic growth in mena countries. Transportation Research Part A: Policy and Practice, 111, 78-95. https://doi.org/10.1016/j.tra.2018.03.013
• Sergi, B. S., D’Aleo, V., Arbolino, R., Carlucci, F., Barilla, D. ve Ioppolo, G. (2020). Evaluation of the italian transport infrastructures: A technical and economic efficiency analysis. Land Use Policy, 99: 104961. https://doi.org/10.1016/j.landusepol.2020.104961
• Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379-423. https://doi.org/10.1145/584091.584093
• Skorobogatova, O. ve Kuzmina-Merlino, I. (2017). Transport infrastructure development performance. Procedia Engineering, 178, 319-329. https://doi.org/10.1016/j.proeng.2017.01.056
• Sowmya Dhanalakshmi, C., Madhu, P., Karthick, A., Mathew, M. ve Vignesh Kumar, R. (2022). A comprehensive mcdm-based approach using topsis and edas as an auxiliary tool for pyrolysis material selection and its application. Biomass Conversion and Biorefinery, 12, 5845-5860. https://doi.org/10.1007/s13399-020-01009-0
• Stenström, C. (2012). Link and effect model for performance improvement of railway infrastructure. Lulea University of Technology.
• Yang, B., Wu, G. ve Yuan, H. (2023). Evaluating the interconnection performance of cross-regional road infrastructures based on an integrated micro-pattern approach with fuzzy linguistic operators. Advanced Engineering Informatics, 57. https://doi.org/10.1016/j.aei.2023.102039
• Yazdani, M., Torkayesh, A. E., Santibanez-Gonzalez, E. D. ve Otaghsara, S. K. (2020). Evaluation of renewable energy resources using integrated shannon entropy—edas model. Sustainable Operations and Computers, 1, 35-42. https://doi.org/10.1016/j.susoc.2020.12.002
• Zavadskas, E. K., Turskis, Z., Antuchevičienė, J. ve Zakarevičius, A. (2012). Optimization of weighted aggregated sum product assessment. Electronics & Electrical Engineering, 6(12), 3-6. https://doi.org/10.5755/j01.eee.122.6.1810
• Zhang, Y. ve Cheng, L. (2023). The role of transport infrastructure in economic growth: Empirical evidence in the UK. Transport Policy, 133, 223-233. https://doi.org/10.1016/j.tranpol.2023.01.017