DEMİRYOLU TAŞIMACILIĞINDA SÜRDÜRÜLEBİLİRLİK STRATEJİLERİNİN Q-ROF BULANIK ORTAMDA ÖNCELİKLENDİRİLMESİ

Öz

Demiryolu taşımacılığında sürdürülebilirlik stratejilerinin belirlenmesi ve önceliklendirilmesi, çevresel, ekonomik ve sosyal boyutlarıyla çok kriterli bir karar verme problemi olarak öne çıkmaktadır. Bu sürecin belirsiz veya yetersiz yürütülmesi; kaynak israfı, artan sera gazı emisyonları, düşen rekabet gücü ve paydaş memnuniyetsizliği gibi önemli sorunlara yol açabilmektedir. Ayrıca, sürdürülebilirlik hedeflerine uyum sağlanamaması sektörde uzun vadeli gelişim ve dönüşümün önünde ciddi bir engel oluşturmaktadır. Bu bağlamda, stratejik kararların bilimsel ve sistematik yaklaşımlarla ele alınması gereklidir. Bu çalışmada, demiryolu taşımacılığında sürdürülebilirlik stratejilerini belirlemek ve önceliklendirmek amacıyla q-rung orthopair fuzzy setlere dayalı SPC (Symmetry Point of Criterion) ve MABAC (Multi-Attributive Border Approximation Area Comparison) yöntemlerinin entegrasyonundan oluşan hibrit bir model önerilmektedir. Bulgulara göre en kritik kriterler sırasıyla istihdama katkı, kamu-özel sektör iş birliği potansiyeli ve toplum ile kullanıcı kabulüdür. Ayrıca, uluslararası ticarette demiryolu rekabetçiliğini artırmak amacıyla hız ve kapasite artırıcı projelere yatırım yapılması gerektiği öne çıkan strateji olmuştur. Elde edilen bu bulgular, sürdürülebilir demiryolu stratejilerinde sosyal boyutun belirleyici bir rol oynadığını ortaya koymaktadır. Özellikle istihdama katkı, kamu-özel sektör iş birliği ve toplumun kabulü gibi kriterlerin öne çıkması, stratejik yatırımların sadece teknik değil, aynı zamanda sosyal etki boyutuyla da şekillendirilmesi gerektiğini göstermektedir.

Anahtar Kelimeler

• Sürdürülebilirlik
• SPC
• MABAC
• Demiryolu Endüstrisi
• Sürdürülebilirlik
• SPC
• MABAC
• q-rungOrthopair Bulanık Kümeler

PRIORITIZING SUSTAINABILITY STRATEGIES IN RAIL TRANSPORT IN Q-ROF FUZZY ENVIRONMENT

Öz

Determining and prioritizing sustainability strategies in railway transportation is a multi-criteria decision-making problem with environmental, economic, and social dimensions. Uncertain or inadequate execution of this process: Waste of resources can lead to significant issues such as increased greenhouse gas emissions, declining competitiveness, and stakeholder dissatisfaction. In addition, failure to comply with sustainability goals is a serious obstacle to long-term development and transformation in the sector. Strategic decisions should be handled with scientific and systematic approaches in this context. In this study, a hybrid model consisting of the integration of SPC (Symmetry Point of Criterion) and MABAC (Multi-Attributive Border Approximation Area Comparison) methods based on q-rung orthopair fuzzy sets is proposed to determine and prioritize sustainability strategies in rail transportation. According to the findings, the most critical criteria are contribution to employment, public-private partnership potential, and community and user acceptance. In addition to increasing railway competitiveness in international trade, investing in projects that increase speed, and capacity has been the prominent strategy. These findings reveal that the social dimension is decisive in sustainable railway strategies. In particular, the prominence of criteria such as contribution to employment, public-private sector cooperation, and public acceptance shows that technical and social impact dimensions should shape strategic investments.

Anahtar Kelimeler

• Railway industry
• Sustainability
• SPC
• MABAC
• q-rung Orthopair Fuzzy Set

Kaynakça

1. Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy sets and Systems, 20(1), 87-96. https://doi.org/https://doi.org/10.1016/S0165-0114(86)80034-3
2. Ayan, B., Abacıoğlu, S., & Basilio, M. P. (2023). A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making. Information (Switzerland) (C.14, Sayı 5). https://doi.org/10.3390/info14050285
3. S., Božanić, D., Pamučar, D., & Marinković, D. (2023). A SPHERICAL FUZZY BASED DECISION MAKING FRAMEWORK WITH EINSTEIN AGGREGATION FOR COMPARING PREPAREDNESS OF SMEs IN QUALITY 4.0. Facta Universitatis, Series: Mechanical Engineering, 21(3), 453. https://doi.org/10.22190/FUME230831037B
4. Biswas, S., Pamucar, D., Dey, P., Chatterjee, S., & Majumder, S. (2024). A q-ROF Based Intelligent Framework for Exploring the Interface Among the Variables of Culture Shock and Adoption Toward Organizational Effectiveness (ss. 255-293). https://doi.org/10.1007/978-981-99-5354-7_14
5. Biswas, S., Sanyal, A., Božanić, D., Kar, S., Milić, A., & Puška, A. (2023). A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers. Entropy, 25(6). https://doi.org/10.3390/e25060905
6. Biswas, S., Sanyal, A., Božanić, D., Puška, A., & Marinković, D. (2023). Critical Success Factors for 5G Technology Adaptation in Supply Chains. Sustainability, 15(6), 5539. https://doi.org/10.3390/su15065539
7. Bouraima, M. B., Qiu, Y., Stević, Ž., & Simić, V. (2023). Assessment of alternative railway systems for sustainable transportation using an integrated IRN SWARA and IRN CoCoSo model. Socio-Economic Planning Sciences, 86. https://doi.org/10.1016/j.seps.2022.101475
8. Deveci, M., Gokasar, I., Pamucar, D., Biswas, S., & Simic, V. (2022). An Integrated Proximity Indexed Value and q-Rung Orthopair Fuzzy Decision-Making Model for Prioritization of Green Campus Transportation. q-Rung Orthopair Fuzzy Sets (ss.303-332). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-1449-2_12

9. Deveci, M., Gokasar, I., Pamucar, D., Coffman, D. M., & Papadonikolaki, E. (2022). Safe E-scooter operation alternative prioritization using a q-rung orthopair Fuzzy Einstein based WASPAS approach. Journal of Cleaner Production, 347. https://doi.org/10.1016/j.jclepro.2022.131239
10. Deveci, M., Pamucar, D., Gokasar, I., Koppen, M., & Gupta, B. B. (2022). Personal Mobility in Metaverse With Autonomous Vehicles Using Q-Rung Orthopair Fuzzy Sets Based OPA-RAFSI Model. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2022.3186294
11. Du, W. S. (2021). A further investigation on q-rung orthopair fuzzy Einstein aggregation operators. Journal of Intelligent and Fuzzy Systems, 41(6). https://doi.org/10.3233/JIFS-210548
12. Feng, F., Zheng, Y., Sun, B., & Akram, M. (2022). Novel score functions of generalized orthopair fuzzy membership grades with application to multiple attribute decision making. Granular Computing, 7(1). https://doi.org/10.1007/s41066-021-00253-7
13. Gandhi, N., & Kant, R. (2023). Evaluation of sustainability performance of the rail freight transportation: An index-based analysis. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2023.01.193
14. Garg, C. P., & Kashav, V. (2020). Assessment of Sustainable Initiatives in the Containerized Freight Railways of India using Fuzzy AHP Framework. Transportation Research Procedia, 48. https://doi.org/10.1016/j.trpro.2020.08.057
15. Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., & Milutinović, A. (2023). Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method)–Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm. CMES - Computer Modeling in Engineering and Sciences, 136(1). https://doi.org/10.32604/cmes.2023.025021
16. Görçün, Ö. Faruk., Chatterjee, Prasenjit., Stević, Željko., & Küçükönder, Hande. (2024). An integrated model for road freight transport firm selection in third-partylogistics using T-spherical Fuzzy sets. Transportation Research Part E: Logistics and Transportation Review, 186, 103542. https://doi.org/10.1016/j.tre.2024.103542
17. Güneri, B., & Deveci, M. (2023). Evaluation of supplier selection in the defense industry using q-rung orthopair fuzzy set based EDAS approach. Expert Systems with Applications, 222. https://doi.org/10.1016/j.eswa.2023.119846
18. Hezam, I. M., Vedala, N. R. D., Kumar, B. R., Mishra, A. R., & Cavallaro, F. (2023). Assessment of Biofuel Industry Sustainability Factors Based on the Intuitionistic Fuzzy Symmetry Point of Criterion and Rank-Sum-Based MAIRCA Method. Sustainability (Switzerland), 15(8). https://doi.org/10.3390/su15086749
19. Krishankumar, R., & Ecer, F. (2023). Selection of IoT service provider for sustainable transport using q-rung orthopair fuzzy CRADIS and unknown weights. Applied Soft Computing, 132, 109870. https://doi.org/10.1016/j.asoc.2022.109870
20. Krmac, E., & Djordjević, B. (2018). An Evaluation of Indicators of Railway Intelligent Transportation Systems using the Group Analytic Hierarchy Process. Electronics Science Technology and Application, 4(2). https://doi.org/10.18686/esta.v4i2.57
21. Kumar, N., & Kumari, N. (2012). Conceptual architectural design of Indian railway intelligent transportation systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7266 LNCS. https://doi.org/10.1007/978-3-642-29667-3_13
22. Lance, G. N., & Williams, W. T. (1966). Computer Programs for Hierarchical Polythetic Classification (“Similarity Analyses”). The Computer Journal, 9(1). https://doi.org/10.1093/comjnl/9.1.60
23. Liu, P., & Wang, P. (2019). Multiple-attribute decision-making based on archimedean bonferroni operators of q-rung orthopair fuzzy numbers. IEEE Transactions on Fuzzy Systems, 27(5). https://doi.org/10.1109/TFUZZ.2018.2826452
24. Liu, Z., & Zhang, Y. (2022). Comprehensive Sustainable Assessment and Prioritization of Different Railway Projects Based on a Hybrid MCDM Model. Sustainability (Switzerland), 14(19). https://doi.org/10.3390/su141912065
25. Mansourianfar, M. H., & Haghshenas, H. (2018). Micro-scale sustainability assessment of infrastructure projects on urban transportation systems: Case study of Azadi district, Isfahan, Iran. Cities, 72. https://doi.org/10.1016/j.cities.2017.08.012
26. Mishra, A. R., & Rani, P. (2021). A q-rung orthopair fuzzy ARAS method based on entropy and discrimination measures: an application of sustainable recycling partner selection. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-021-03549-3
27. Noruzi, M., Naderan, A., Zakeri, J. A., & Rahimov, K. (2023). A Novel Decision-Making Framework to Evaluate Rail Transport Development Projects Considering Sustainability under Uncertainty. Sustainability (Switzerland), 15(17). https://doi.org/10.3390/su151713086
28. Peng, X., & Dai, J. (2018). Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications, 29(10). https://doi.org/10.1007/s00521-016-2607-y
29. Qahtan, S., Alsattar, H. A., Zaidan, A. A., Deveci, M., Pamucar, D., & Delen, D. (2023). Performance assessment of sustainable transportation in the shipping industry using a q-rung orthopair fuzzy rough sets-based decision making methodology. Expert Systems with Applications, 223. https://doi.org/10.1016/j.eswa.2023.119958
30. Rani, P., Mishra, A. R., Pamucar, D., Ali, J., & Hezam, I. M. (2023). Interval-valued intuitionistic fuzzy symmetric point criterion-based MULTIMOORA method for sustainable recycling partner selection in SMEs. Soft Computing. https://doi.org/10.1007/s00500-023-08189-7
31. Rao, S. H. (2021). Transportation synthetic sustainability indices: A case of Taiwan intercity railway transport. Ecological Indicators, 127. https://doi.org/10.1016/j.ecolind.2021.107753
32. Saha, A., Majumder, P., Dutta, D., & Debnath, B. K. (2021). Multi-attribute decision making using q-rung orthopair fuzzy weighted fairly aggregation operators. Journal of Ambient Intelligence and Humanized Computing, 12(7). https://doi.org/10.1007/s12652-020-02551-5
33. Simionescu, V., & Silvius, G. (2016). Assessing Sustainability of Railway Modernization Projects; A Case Study from Romania. Procedia Computer Science, 100. https://doi.org/10.1016/j.procs.2016.09.182
34. Stoilova, S., Munier, N., Kendra, M., & Skrúcaný, T. (2020). Multi-criteria evaluation of railway network performance in countries of the TEN-T orient-east med corridor. Sustainability (Switzerland), 12(4). https://doi.org/10.3390/su12041482
35. Wang, J., Sekei, V. S., Ganiyu, S. A., & Makwetta, J. J. (2021). Research on the sustainability of the standard gauge railway construction project in Tanzania. Sustainability (Switzerland), 13(9). https://doi.org/10.3390/su13095271
36. Wang, P., Wang, J., Wei, G., & Wei, C. (2019). Similarity measures of q-rung orthopair fuzzy sets based on cosine function and their applications. Mathematics, 7(4). https://doi.org/10.3390/math7040340
37. Wang, R., & Li, Y. (2018). A novel approach for green supplier selection under aq-rung orthopair fuzzy environment. Symmetry, 10(12). https://doi.org/10.3390/sym10120687
38. Weber, S. (1983). A general concept of fuzzy connectives, negations and implications based on t-norms and t-conorms. Fuzzy Sets and Systems, 11(1-3). https://doi.org/10.1016/S0165-0114(83)80073-6
39. Wei, G., Gao, H., & Wei, Y. (2018). Some q-rung orthopair fuzzy Heronian mean operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(7). https://doi.org/10.1002/int.21985
40. Yager, R. R. (2013). Pythagorean fuzzy subsets. Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
41. Yager, R. R. (2017). Generalized Orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222-1230. https://doi.org/10.1109/TFUZZ.2016.2604005
42. Yin, S., Wang, Y., & Shafiee, S. (2023). Ranking products through online reviews considering the mass assignment of features based on BERT and q-rung orthopair fuzzy set theory. Expert Systems with Applications, 213. https://doi.org/10.1016/j.eswa.2022.119142
43. Yüksel, Ç., & Uncu, N. (2024). Evaluating the Performance of Railway Transportation Companies Using Multi-Criteria Decision-Making Methods. Demiryolu Mühendisliği, 20, 11-24. https://doi.org/10.47072/demiryolu.1407420