Çok Kriterli Karar Verme Problemlerinde Duyarlılık Analizi

Year 2022, , 1025 - 1056, 28.12.2022

Gülay Demir , Rahim Arslan

https://doi.org/10.26745/ahbvuibfd.1103531

Cited By: 10

Abstract

Bu çalışmanın amacı, çok kriterli karar verme (ÇKKV) yöntemlerinin uygulanmasında kullanılan çalışmalar için bir kararlılık ve duyarlılık modeli önermektir. Bu kapsamda kararlılık ve duyarlılık analizi için “kriter ağırlığının varyasyonuna dayalı duyarlılık analizi, sıra ters çevirme özelliğine dayalı duyarlılık analizi ve farklı sıralama metotlarından elde edilen sonuçlar ile karşılaştırma analizi” adımlarının birlikte kullanılması önerilmiştir. Metodun uygulama kısmında alternatif olarak Kırılgan Beşli ülkeleri, bu ülkelere ait işsizlik oranı, devlet bütçesi, GSYİH büyümesi, enflasyon, cari hesap dengesi, risk primi kriter olarak kullanılmıştır. Kriterler MEREC ile ağırlıklandırılmış, alternatiflerin sıralanması ise WISP ile gerçekleştirilmiştir. Metodun uygulama safhasında 22 senaryo üzerinden kriterlere atanan farklı ağırlıklar ile modelin ağırlık katsayılarındaki değişikliklere duyarlı olduğu bulunmuştur. Modelin sıra ters çevirme adımında oluşturulan 4 farklı senaryo üzerinden modelin dinamik bir ortamda geçerli sonuçlar sağladığı görülmüştür. MEREC-WISP tabanlı modelin güvenilirliği için PIV, CoCoSo, COPRAS, WEDBA, TOPSIS ve SAW gibi yaygın olarak kullanılan bazı yöntemlerle bir sıralama karşılaştırması yapılmış ve sonuçların yüksek korelasyona sahip olduğu görülmüştür.

Keywords

Duyarlılık ve Kararlılık Analizi, Bibliyometri Analizi, ÇKKV.

Sensitivity Analysis in Multi-Criterion Decision-Making Problems

Abstract

The aim of this study is to propose a model of stability and sensitivity for the studies used in the implementation of multi-criteria decision making (MCDM). In this context, it is proposed to use the steps "sensitivity analysis based on the variation of criterion weight, sensitivity analysis based on sequence reversal feature and comparison analysis with results from different sorting methods" for stability and sensitivity analysis. In the implementation part of the method, the Fragile Five countries were used as criteria for the unemployment rate, state budget, GDP growth, inflation, current account balance, risk premium for these countries. The criteria were weighted with MEREC and the ordering of alternatives was carried out with WISP. In the application phase of the method, it was found that the model was sensitive to changes in weight coefficients with different weights assigned to criteria over 22 scenarios. It has been observed that the model provides valid results in a dynamic environment through 4 different scenarios created in the sequence inversion step of the model. For the reliability of the MEREC-WISP-based model, a ranking comparison was made with some commonly used methods such as PIV, CoCoSo, COPRAS, WEDBA, TOPSIS and SAW and the results were found to have a high correlation.

Keywords

Sensitivity and Stability Analysis, Bibliometrics Analysis, MCDM.

References

• Amin, M., Javed, S. A., Liu, S. & Deng, X. (2020). “Distinguishing coefficient driven sensitivity analysis of GRA model for intelligent decisions: application in project management”, Technological and Economic Development of Economy, 26(3), 621-641.
• Antanasijević, D., Pocajt, V., Ristić, M., & Perić-Grujić, A. (2017). “A differential multi-criteria analysis for the assessment of sustainability performance of European countries: Beyond country ranking”, Journal of Cleaner Production, 165, 213-220.
• Arsu, T. & Ayçin, E. (2021). “Evaluation of OECD countries with multi-criteria decision-making methods in terms of economic, social and environmental aspects”, Operational Research in Engineering Sciences: Theory and Applications, 4(2), 55-78.
• Belke, M. (2020). “CRITIC ve MAIRCA yöntemleriyle G7 ülkelerinin makroekonomik performansının değerlendirilmesi”, İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, Prof. Dr. Sabri ORMAN Özel Sayısı, 120-139.
• Blagojević, A., Kasalica, S., Stević, Ž., Tričković, G. ve Pavelkić, V. (2021). “Evaluation of Safety Degree at Railway Crossings in Order to Achieve Sustainable Traffic Management: A Novel Integrated Fuzzy MCDM Model”, Sustainability, 13, 832.
• Boyacı, A. Ç., & Şişman, A. (2022). “Pandemic hospital site selection: a GIS-based MCDM approach employing Pythagorean fuzzy sets”, Environmental Science and Pollution Research, 29(2), 1985-1997.
• Costa, A. S., Rui Figueira, J., Vieira, C. R., & Vieira, I. V. (2019). “An application of the ELECTRE TRI‐C method to characterize government performance in OECD countries”, International Transactions in Operational Research, 26(5), 1935-1955.
• Ecer, F. (2021). “Sustainability assessment of existing onshore wind plants in the context of triple bottom line: a best-worst method (BWM) based MCDM framework”, Environmental Science and Pollution Research, 28, 19677-19693.
• Erdogan, N., Pamucar, D., Kucuksarı, S. & Deveci, M. (2021). “An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations”, Applied Energy, 304, 117866.
• Eyupoglu, K. (2016). “Comparison of developing countries’ macro performances with AHP and TOPSIS methods”, Çankırı Karatekin University Journal of The Faculty of Economics and Administrative Sciences, 6(1), 131-146.
• Feng, J., Xu, S. X. & Li, M. (2021). “A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective”, Sustainable Cities and Society, 65, 102623.
• Gorcun, O. F., Senthil, S., & Küçükönder, H. (2021). “Evaluation of tanker vehicle selection using a novel hybrid fuzzy MCDM technique”, Decision Making: Applications in Management and Engineering, 4(2), 140-162.
• Goswami, S. S., Mohanty, S. K. & Behera, D. K. (2022). “Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool”, Materials Today: Proceedings, 52(3), 1153-1160.
• Hwang, C. L. & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making. Berlin, Heidelberg, Springer.
• Keshavarz‑Ghorabaee, M. (2021). “Assessment of distribution center locations using a multi‑expert subjective–objective decision‑making approach”, Scientifc Reports, 11, 1-19.
• 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.
• Kumar, R. R., Kumari, B. & Kumar, C. (2021). “CCS-OSSR: A framework based on Hybrid MCDM for Optimal Service Selection and Ranking of Cloud Computing Services”, Cluster Computing, 24, 867-883.
• Kuncova, M. & Seknickova, J. (2021). “Two-stage weighted PROMETHEE II with results’ visualization” Central European Journal of Operations Researc, 30, 547-571.
• Lo, H.-W., Hsu, C.-C., Chen, B.-C. & Liou, J. J. H. (2021). “Building a grey-based multi-criteria decision-making model for offshore wind farm site selection”, Sustainable Energy Technologies and Assessments, 43, 100935.
• Maccrimmon, K.R. (1968). “Descriptive and normative implications of the decision-theory postulates”, In: Borch K., Mossin J. (eds) Risk and Uncertainty. International Economic Association Conference Volumes, 1–50. Palgrave Macmillan, London.
• Mufazzal, S., & Muzakkir, S. M. (2018). “A new multi-criterion decision making (MCDM) methodbased on proximity indexed value for minimizing rank reversals”, Computers & Industrial Engineering, 119, 427-438.
• Pamučar, D., & Ćirović G. (2015). “The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison”, Expert Systems with Applications, 42(6), 3016-3028.
• Rao, R. V. & Singh, D. (2012). “Evaluating flexible manufacturing systems using Euclidean distance-based integrated approach. International Journal of Decision Sciences”, Risk and Management, 3(1-2), 32-53.
• Rashid T, Ali A, Chu Y-M. (2021). “Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection”, PLoSONE, 16(2), 1-18.
• Rosas, S. R., Kagan, J. M., Schouten, J. T., Slack, P. A., Trochim, W. M. (2011). “Evaluating research and impact: a bibliometric analysis of research by the Nih/Niaid Hiv/aids clinical trials networks”, PLoS One, 6(3), 1-12.
• Stanujkić, D., Popović, G., Karabasević, D., Meidute-Kavaliauskiene, I. & Ulutaş, A. (2021). “An Integrated Simple Weighted Sum Product Method-WISP”, IEEE Trans. Eng. Manag., 1-12.
• Stanujkić, D., Karabašević, D., Popović, G., Zavadskas, E.K., Saračević, M., Stanimirović, P.S., Ulutaş, A., Katsikis, V.N., Meidute-Kavaliauskiene, I. (2021). “Comparative Analysis of the Simple WISP and Some Prominent MCDM Methods: A Python Approach”, Axioms, 10(4), 1-14.
• Stević, 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.
• Trading Economics, (2021). https://tradingeconomics.com/ (Erişim Tarihi: 14.12.2021).
• Wang, H.N., Nguyen, N. A. T., Dang, T. T. & Hsu, H. P. (2021). “Evaluating Sustainable Last-Mile Delivery (LMD) in B2C E-Commerce Using Two-Stage Fuzzy MCDM Approach: A Case StudyFrom Vietnam”, IEEE Access, 9, 146050-146067.
• World Government Bonds, (2021). http://www.worldgovernmentbonds.com/ (Erişim Tarihi: 14.12.2021).
• Yazdani, M., Zarate, P., Kazimieras Zavadskas, E. & Turskis, Z. (2019). “A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems”, Management Decision, 57(9), 2501-2519.
• Zavadskas, E. K. & Kaklauskas, A. (1996). “System technical evaluation of buildings”, (Pastatų sistemotechninis įvertinimas). Vilnius: Technika, (in Lithuanian).
• Zhang, X., Wang, C., Li, E. & Xu, C. (2014). “Assessment model of eco environmental vulnerability based on improved Entropy weight method”, The Scientific World Journal, 797814, 1-7.