Kapsayıcı Kampüs Ortamı Tasarımı Kriterlerinin FANP ve CFPR Metodolojileri Kullanılarak Değerlendirilmesi

Öz

Üniversite kampüsleri farklı sosyo-kültürel ortamlardan gelen bireyleri bir araya getirmektedir. Aynı zamanda üniversite kampüsleri, bireylerin kişisel ve entelektüel gelişimlerine katkı yapmakta ve bir sosyalleşme alanı işlevi de görmektedir. Kampüsler, sosyal, kültürel, ekonomik ve mekânsal etkileri ile canlılık yaratmaktadır. Bu bağlamda, üniversite kampüslerinin herkesin erişebileceği mekanlar olarak kapsayıcı tasarım çerçevesinde tasarlanması gerekmektedir. Kapsayıcı üniversite tasarımının yedi kriteri; "Arazi Kullanım Organizasyonu", "Kompaktlık", " Bağlanabilirlik", " Konfigürasyon", "Yaşayan kampüs", "Yeşillik" ve "Bağlam"dır. Bu ilkelerin önceliklendirilmesi veya ağırlıklandırılması, Çok Kriterli Karar Verme (ÇKKV) problemi olarak ele alınabilir. Bu nedenle bu çalışmada, bu kriterlerin değerlendirilmesinde Bulanık Analitik Ağ Süreci (FANP) ve Tutarlı Bulanık Tercih İlişkileri (CFPR) metodolojileri kullanılmış ve her iki metodolojinin sonuçları karşılaştırılmıştır.

Anahtar Kelimeler

• Kapsayıcı Tasarım
• Kampüs Ortamı
• Çok Kriterli Karar Verme
• CFPR
• Bulanık ANP

Evaluating Inclusive Campus Environment Design Criteria Using FANP and CFPR Methodologies

Öz

University campuses bring together individuals from different socio-cultural backgrounds. At the same time, university campuses contribute to the personal and intellectual development of individuals and serve as a socialization area. Campuses create vitality with their social, cultural, economic, and spatial effects. In this context, university campuses should be designed within the framework of inclusive design as publicly accessible spaces. Seven Inclusive Campus Environment Design Criteria are “Land Use Organization”, “Compactness”, “Connectivity”, “Configuration”, “Living campus”, “Greens” and “Context”. The prioritization or weighting of these principles can be addressed as a Multi-Criteria Decision Making (MCDM) problem. For this reason, in this paper, Fuzzy Analytic Network Process (FANP) and Consistency Fuzzy Preference Relations (CFPR) methodologies are used for the evaluation of these criteria and the results of both methodologies are compared.

Anahtar Kelimeler

• Inclusive Design
• Campus Environment
• Multi Criteria Decision Making
• CFPR
• Fuzzy ANP

Kaynakça

1. [1] Mace, R. 1985. Universal Design: Barrier Free Environments for Everyone. Designers West, Cilt. 33, s. 147-152.
2. [2] Prellwitz, M. 2007. Playgrounds Accessibility and Usability for Children with Disabilities. Ph.D. Thesis, Luleå University of Technology Department of Human Work Sciences, Sweden.
3. [3] Özdemir, Ş. 2019. Üniversite Kampüslerinin Kapsayıcı Tasarım Kavramına Uygun Hale Getirilmeleri İçin Bir Değerlendirme Aracı Önerisi. Yıldız Teknik Üniversitesi, Doktora Tezi, İstanbul.
4. [4] Direk, S.Y., Erdoğan, F. 2016. Kentlerde Engelleştirilen Çocuklar ve Geleneksel Oyun Kültürü. Herkes İçin Tasarım ve Erişilebilir Kentler 2. Ulusal Engellileştirilenler Sempozyumu, 7-9 Nisan, Konya.
5. [5] Kenney, D.R., Dumont, R., Kenney, G.S. 2005. Mission and Place: Strengthening Learning and Community Through Campus Design. CT: Praeger Publishers, Westport.
6. [6] Pohekar, S.D., Ramachandran, M. 2004. Application of Multi-Criteria Decision Making to Sustainable Energy Planning—A Review, Renewable and Sustainable Energy Reviews, Cilt. 8, s. 365-381. DOI: 10.1016/j.rser.2003.12.007
7. [7] Antucheviciene, J., Kala, Z., Marzouk, M., Vaidogas. E.R. 2015. Solving Civil Engineering Problems By Means of Fuzzy and Stochastic MCDM Methods: Current State and Future Research, Mathematical Problems in Engineering. DOI: 10.1155/2015/362579
8. [8] Mulliner, E., Malys, N., Maliene, V. 2016. Comparative Analysis of MCDM Methods for the Assessment of Sustainable Housing Affordability, Omega, Cilt. 59, s. 146-156. DOI: 10.1016/j.omega.2015.05.013

9. [9] Lee, H.C., Chang, C.T. 2018. Comparative Analysis of MCDM Methods for Ranking Renewable Energy Sources in Taiwan, Renewable and Sustainable Energy Reviews, Cilt. 92, s. 883-896. DOI: 10.1016/j.rser.2018.05.007
10. [10] Wu, Y., Xu, C., Zhang, T. 2018. Evaluation of Renewable Power Sources Using A Fuzzy MCDM Based on Cumulative Prospect Theory: A Case in China, Energy, Cilt. 147, s. 1227-1239. DOI: 10.1016/j.energy.2018.01.115
11. [11] Awan, M.A., Ali, Y. 2019. Sustainable Modeling in Reverse Logistics Strategies Using Fuzzy MCDM, Management of Environmental Quality: An International Journal, Cilt. 30, s. 1132-1151.
12. [12] Jahangiri, M., Shamsabadi, A.A., Mostafaeipour, A., Rezaei, M., Yousefi, Y., Pomares, L.M. 2020. Using Fuzzy MCDM Technique to Find The Best Location in Qatar for Exploiting Wind and Solar Energy to Generate Hydrogen and Electricity, International Journal of Hydrogen Energy, Cilt. 45, s. 13862-13875. DOI: 10.1016/j.ijhydene.2020.03.101
13. [13] Karaşan, A., Kaya, I., Erdoğan, M. 2020. Location Selection of Electric Vehicles Charging Stations by Using A Fuzzy MCDM Method: A Case Study in Turkey, Neural Computing and Applications, Cilt. 32, s. 4553-4574.
14. [14] Ozdemir, Y., Ozdemir, S. 2020a. Residential Heating System Selection Using MCDM Techniques. Editors: Elias M. Heating Systems: Design, Applications and Technology, Nova Science Publishers.
15. [15] Özcan, İ., İnan, U.H., Korkusuz, A.Y. 2020. Çok Kriterli Karar Verme Yöntemleriyle Metro Sürücüsü Seçimi, Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, Cilt. 15, s. 1185-1202.
16. [16] Opricovic, S., Tzeng, G.H. 2004. Compromise Solution by MCDM Methods: A Comparative Analysis of VIKOR and TOPSIS, European Journal of Operational Research, Cilt. 156, s. 445-455.
17. [17] Ozdemir, Y., Nalbant, K.G., Basligil, H. 2017. Evaluation of Personnel Selection Criteria Using Consistent Fuzzy Preference Relations, International Journal of Management Science, Cilt. 4, s. 76-81.
18. [18] Alias, F.M.A., Abdullah, L., Gou, X., Liao, H., Herrera-Viedma, E. 2019. Consistent Fuzzy Preference Relation with Geometric Bonferroni Mean: A Fused Preference Method for Assessing The Quality Of Life, Applied Intelligence, Cilt. 49, s. 2672-2683.
19. [19] Park, Y.I., Lu, W., Nam, T.H., Yeo, G.T. 2019. Terminal Vitalization Strategy through Optimal Route Selection Adopting CFPR Methodology, The Asian Journal of Shipping and Logistics, Cilt. 35, s. 41-48. DOI: 10.1016/j.ajsl.2019.03.006
20. [20] Huynh, N.X., Phi, H.D. 2020. Applying Consistency Fuzzy Preference Relations to Select a Strategy that Attracts Foreign Direct Investment (FDI) in Developing Supporting Industries for Vietnam, In Foreign Direct Investment Perspective Through Foreign Direct Divestment, IntechOpen.
21. [21] Ozdemir, Y., Nalbant, K.G. 2020. Personnel Selection for Promotion Using An Integrated Consistent Fuzzy Preference Relations-Fuzzy Analytic Hierarchy Process Methodology: A Real Case Study, Asian Journal of Interdisciplinary Research, Cilt. 3, s. 219-236.
22. [22] Govindan, K., Shankar, K.M., Kannan, D. 2016. Application of Fuzzy Analytic Network Process for Barrier Evaluation in Automotive Parts Remanufacturing Towards Cleaner Production–A Study in an Indian Scenario, Journal of Cleaner Production, Cilt. 114, s. 199-213. DOI: 10.1016/j.jclepro.2015.06.092
23. [23] Liao, H., Mi, X., Xu, Z., Xu, J., Herrera, F. 2018. Intuitionistic Fuzzy Analytic Network Process, IEEE Transactions on Fuzzy Systems, Cilt. 26, s. 2578-2590.
24. [24] Mohammadzadeh, A.K., Ghafoori, S., Mohammadian, A., Mohammadkazemi, R., Mahbanooei, B., Ghasemi, R. 2018. A Fuzzy Analytic Network Process (FANP) Approach for Prioritizing Internet of Things Challenges in Iran, Technology in Society, Cilt. 53, s. 124-134. DOI: 10.1016/j.techsoc.2018.01.007
25. [25] Ozdemir, S., Ozdemir, Y. 2018. Prioritizing Store Plan Alternatives Produced with Shape Grammar Using Multi-Criteria Decision-Making Techniques, Environment and Planning B: Urban Analytics and City Science, Cilt. 45, s. 751-771.
26. [26] Li, Y., Wang, X. 2019. Using Fuzzy Analytic Network Process and ISM Methods for Risk Assessment of Public-Private Partnership: A China Perspective, Journal of Civil Engineering and Management, Cilt. 25, s. 168-183. DOI: 10.3846/jcem.2019.8655
27. [27] Ozdemir, Y., Ozdemir, S. 2020b. Weighting The Universal Design Principles using Multi-Criteria Decision Making Techniques, Mühendislik Bilimleri ve Tasarım Dergisi, Cilt. 8, s. 105-118.
28. [28] Hemmati, N., Galankashi, M.R., Imani, D.M., Farughi, H. 2018. Maintenance Policy Selection: A Fuzzy-ANP Approach, Journal of Manufacturing Technology Management, Cilt. 29, s. 1253-1268.
29. [29] Danai, H., Hashemnia, S., Ahmadi, R., Bazazzadeh, S.H. 2019. Application of Fuzzy ANP Method to Select The Best Supplier in The Supply Chain, International Journal of Operational Research, Cilt. 35, s. 1-19. DOI: 10.1504/IJOR.2019.099540
30. [30] Alilou, H., Rahmati, O., Singh, V.P., Choubin, B., Pradhan, B., Keesstra, S., Sadeghi, S.H. 2019. Evaluation of Watershed Health using Fuzzy-ANP Approach Considering Geo-Environmental and Topo-Hydrological Criteria, Journal of Environmental Management, Cilt. 232, s. 22-36. DOI: 10.1016/j.jenvman.2018.11.019
31. [31] Galankashi, M.R., Rafiei, F.M., Ghezelbash, M. 2020. Portfolio Selection: A Fuzzy-ANP Approach, Financial Innovation, Cilt. 6, s. 1-34.
32. [32] Hajrasouliha, A.H. 2015. The Morphology of The “Well-Designed Campus”: Campus Design for A Sustainable and Livable Learning Environment, Ph.D. Thesis, The University of Utah, Utah.
33. [33] Coulson, J., Roberts, P., Taylor, I. 2010. University Planning and Architecture: The Search for Perfection. Abington, Routledge.
34. [34] Haar, S. 2011. The City as Campus: Urbanism and Higher Education in Chicago. Minneapolis, University of Minnesota Press.
35. [35] Saaty, T.L. 1996. Decision Making with Dependence and Feedback The Analytic Network Process. Pittsburgh. RWS Publications.
36. [36] Rezaeiniya, N., Ghadikolaei, A.S., Mehri-Tekmeh, J., Rezaeiniya, H. 2014. Fuzzy ANP Approach For New Application: Greenhouse Location Selection; A Case in Iran, Journal of Mathematics and Computer Science, Cilt. 8, s. 1-20.
37. [37] Meade, L., Sarkis, J. 1998. Strategic Analysis of Logistics and Supply Chain Management Systems using The Analytical Network Process, Transportation Research Part E: Logistics and Transportation Review, Cilt. 34, s. 201-215. DOI: 10.1016/S1366-5545(98)00012-X
38. [38] Hsieh, T.Y., Lu, S.T., Tzeng, G.H. 2004. Fuzzy MCDM Approach for Planning and Design Tenders Selection in Public Office Buildings, International Journal of Project Management, Cilt. 22, s. 573-584. DOI: 10.1016/j.ijproman.2004.01.002
39. [39] Haghighi, M., Divandari, A., Keimasi, M. 2010. The Impact of 3D E-Readiness on E-Banking Development in Iran: A Fuzzy AHP Analysis, Expert Systems with Applications, Cilt. 37, s. 4084-4093. DOI: 10.1016/j.eswa.2009.11.024
40. [40] Kaya, T., Kahraman, C. 2011. An Integrated Fuzzy AHP–ELECTRE Methodology for Environmental Impact Assessment, Expert Systems with Applications, Cilt. 38, s. 8553-8562. DOI: 10.1016/j.eswa.2011.01.057
41. [41] Yasmin, F., Kumar, A., Kumar, A. 2013. Fuzzy Theory Concept Applied in Analytic Network Process, International Journal of Advanced Research in Computer Science and Software Engineering, Cilt. 3, s. 832-837.
42. [42] Herrera-Viedma, E., Herrera, F., Chiclana, F., Luque, M. 2004. Some Issues on Consistency of Fuzzy Preference Relations, European Journal of Operational Research, Cilt. 154, s. 98-109. DOI: 10.1016/S0377-2217(02)00725-7
43. [43] Chang, T.H., Hsu, S.C., Wang, T.C. 2013. A Proposed Model for Measuring The Aggregative Risk Degree of Implementing An RFID Digital Campus System with The Consistent Fuzzy Preference Relations, Applied Mathematical Modelling, Cilt. 37, s. 2605-2622. DOI: 10.1016/j.apm.2012.06.029
44. [44] Wang, T.C., Lin, Y.L. 2009. Applying The Consistent Fuzzy Preference Relations to Select Merger Strategy for Commercial Banks in New Financial Environments, Expert Systems with Applications, Cilt. 36, s. 7019-7026. DOI: 10.1016/j.eswa.2008.08.023
45. [45] Jafarnejad, A., Ebrahimi, M., Abbaszadeh, M.A., Abtahi, S.M. 2014. Risk Management in Supply Chain using Consistent Fuzzy Preference Relations, International Journal of Academic Research in Business and Social Sciences, Cilt. 4, s. 77-85. DOI: 10.6007/IJARBSS/v4-i1/514