COMBINING DIFFERENT MCDM METHODS WITH THE COPELAND METHOD: AN INVESTIGATION ON MOTORCYCLE SELECTION

Aşkın Özdağoğlu; Murat Kemal Keleş; Anıl  Altınata; Alptekin Ulutaş

doi:10.5937/jpmnt9-34120

Aşkın Özdağoğlu Dokuz Eylül University, Business Faculty, Business Department, Tinaztepe Campus
Murat Kemal Keleş Isparta University of Applied Sciences, Keciborlu Vocational School, Transportation Services Department
Anıl Altınata Dokuz Eylül University, Business Faculty, Business Department, Tinaztepe Campus
Alptekin Ulutaş Sivas Cumhuriyet University, Faculty of Economics and Administrative Sciences, International Trade and Logistics

DOI: https://doi.org/10.5937/jpmnt9-34120

Keywords: Multi Criteria Decision Making (MCDM), Copeland, Pivot Pairwise Relative Criteria Importance Assessment (PIPRECIA), Multi Objective Performance Analysis (MOPA), ), Multi Objective Optimization on The Basis of Simple Ratio Analysis (MOOSRA), Complex Proportional Assessment (COPRAS), Simple Additive Weighting (SAW), Weighted Product Method (WPM), Range of Value (ROV), Motorcycle Selection

Abstract

There are many different multi-criteria decision making methods in the literature. These methods, which enable criteria with different measurement units to be examined together, allow choosing between alternatives. However, different methods can produce different results depending on the data set. The aim of this study is to combine the results obtained by applying different methods to the data set with the Copeland method. To this end, a problem with real data was first addressed. Technical data of motorcycle alternatives that can be preferred for individual needs were collected in terms of different criteria. The weights of these criteria were found by the PIPRECIA method. Six different multi-criteria decision making methods were used to evaluate motorcycle alternatives. These methods are MOPA, MOOSRA, COPRAS, SAW, WPM and ROV. The sequencing results obtained from these methods were combined with the Copeland method and the results were discussed.

References

Adalı, E. A., & Işık, A. T. (2017). The multi-objective decision-making methods based on MULTIMOORA and MOOSRA for the laptop selection problem. Journal of Industrial Engineering International, 13. 229–237. DOI 10.1007/s40092-016-0175-5.

Akarpa, A., & Diler, H. (2020). Sanat stil ilişkisi üzerine motosiklet tasarımı üzerinde bir araştırma. Mediterranean Journal of Humanities, 10(1), 1–10. DOI: 10.13114/MJH.2020.513.

Ardahan, F., & Güleç, S. (2020). Bireyleri motosiklet kullanmaya motive eden faktörlerin çeşitli değişkenlere göre karşılaştırılması. Elektronik Sosyal Bilimler Dergisi, 19(73), 237-253.

Balusa, B. C., & Singam, J. (2018). Underground mining method selection using WPM and PROMETHEE. Journal of the Institution of Engineers (India): Series D, 99(1), 165-171. https://doi.org/10.1007/s40033-017-0137-0.

Bilgin, H.Y. (2018). Yakıt Türüne Göre Motosiklet Seçimi ve Yeni Bir Tasarım. Başkent Üniversitesi Fen Bilimleri Enstitüsü, Yayımlanmamış Yüksek Lisans Tezi.

Çakır, E. (2017). Kriter Ağırlıklarının SWARA – Copeland Yöntemi ile Belirlenmesi: Bir Üretim İşletmesinde Uygulama. Adnan Menderes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt 4, Sayı 1, (42-56).

Chatterjee, P., Athawale, V. M., & Chakraborty, S. (2011). Materials selection using complex proportional assessment and evaluation of mixed data methods. Materials and Design, 32, 851-860.

Dey, B., Bairag, B., Sarkar, B., & Sanyal, S.K. (2016). Multi objective performance analysis: A novel multi-criteria decision making approach for a supply chain, Computers & Industrial Engineering, 94, 105–124. Doi: 10.1016/j.cie.2016.01.019.

Ertaş, M., & Aktaş, G. (2017). Sosyal kimlik kuramı: motosiklet kullanıcılarının i̇ncelenmesi. 4. Disiplinlerarası Turizm Araştırmaları Kongresi, 9 – 12 Kasım 2017, Kuşadası, Aydın. 240-247.

Honda, (2021). Teknik Özellikler. https://honda.com.tr/motosiklet/modeller/supersport/cbr1000rr-r-fireblade-sp (21.01.2021).

Jauković-Jocić, K., Karabašević, D., & Jocić, G. (2020). The use of the PIPRECIA method for assessing the quality of e-learning materials. Ekonomika, 66(3), 37-45. doi: 10.5937/ekonomika2003037J.

Jha, G. K., Chatterjee, P., Chatterjee, R., & Chakraborty, S. (2013). Suppliers Selection in Manufacturing Environment using Range of Value Method. i-Manager's Journal on Mechanical Engineering, 3(3), 15-22.

Karakuş, C. B., Demiroğlu, D., Çoban, A., & Ulutaş, A. (2020). Evaluation of GIS-based multi-criteria decision-making methods for sanitary landfill site selection: the case of Sivas city, Turkey. Journal of Material Cycles and Waste Management, 22(1), 254-272. https://doi.org/10.1007/s10163-019-00935-0.

Kawasaki, (2021). Teknik Detaylar. http://www.kawasaki.com.tr/motosiklet/NinjaZx-10R/ZX10R-teknik.html (21.01.2021).

Kurtipek, A., & Akbulut, D. (2021). At binicilik takımlarının motosiklet kültürü üzerindeki yansımaları. Kültür Araştırmaları Dergisi, (8), 155-174. DOI: 10.46250/kulturder.857828.

Lucci, C., Piantini, S., Savino, G., & Pierini, M. (2021). Motorcycle helmet selection and usage for improved safety: a systematic review on the protective effects of helmet type and fastening. Traffic injury prevention, 1-11. https://doi.org/10.1080/15389588.2021.1894640.

Madić, M., Radovanović, M. & Miodrag. M. (2016). Application of the ROV method for the selection of cutting fluids. Decision Science Letters, 5 (2), 245–254. doi: 10.5267/j.dsl.2015.12.001.

Meshram, S. G., Alvandi, E., Meshram, C., Kahya, E., & Al-Quraishi, A. M. F. (2020). Application of SAW and TOPSIS in prioritizing watersheds. Water Resources Management, 34(2), 715-732. https://doi.org/10.1007/s11269-019-02470-x.

Nezhad, M.R.G., Zolfani, S.H., Moztarzadeh, F., Zavadskas, E.K. & Bahrami, M. (2015). Planning the priority of high tech industries based on SWARA-WASPAS methodology: The case of the nanotechnology industry in Iran. Economic Research-Ekonomska Istraživanja, 28(1), 1111-1137. DOI: 10.1080/1331677X.2015.1102404.

Ömürbek, N., Karaatlı, M., & Balcı, H.F. (2016). Entropi Temelli MAUT ve SAW Yöntemleri İle Otomotiv Firmalarının Performans Değerlemesi. Dokuz Eylül Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 31(1), 227-255.

Özdağoğlu, A. (2013). İmalat işletmeleri için eksantrik pres alternatiflerinin COPRAS yöntemi ile karşılaştırılması. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 4(8), 1-22.

Özdağoğlu, A., & Keleş, M.K. (2019). DEMATEL ve MOOSRA Bütünleşik Yaklaşımıyla Tedarikçi Seçimi: Mobilya Fabrikasında Bir Uygulama. II. Uluslararası İşletme ve Organizasyon Kongresi, 4-5-6 Eylül 2019, İzmir, 1641-1656.

Özdağoğlu, A., & Keleş, M.K. (2019). Spor Yönetimi açısından Gri Entropi Tabanlı Rov Yöntemi İle 4 büyük futbol kulübünün finansal performans analizi. Süleyman Demirel Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, (35), 107-123.

Özdağoğlu, A., Yakut, E., & Bahar, S. (2017). Machine selection in a dairy product company with entropy and SAW methods integration. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 32(1), 341-359.

Risdiyanto, Munawar, A., Irawan, M. Z., & Nugraha, A. A. (2020). Model selection of online motorcycle taxi and motorcycle modes on work trips. In IOP Conference Series: Materials Science and Engineering (Vol. 1007). 3rd Tarumanagara International Conference of the Applications of Technology and Engineering (TICATE) 2020 3-4 August 2020, Jakarta, Indonesia, IOP Publishing. doi:10.1088/1757-899X/1007/1/012059.

Rojniruttikul, N. (2017). A multi-criteria approach for supplier selection: Case of motorcycle parts manufacturer in Thailand. Review of Integrative Business and Economics Research, 6(3), 188–193.

Sarkar, A., Panja, S. C., Das, D., & Sarkar, B. (2015). Developing an Efficient Decision Support System for Non-Traditional Machine Selection: An Application of MOORA and MOOSRA. Production & Manufacturing Research, 3(1), 324-342. DOI.org/10.1080/21693277.2014.895688.

Savitha, K., & Chandrasekar, C. (2011). Vertical Handover decision schemes using SAW and WPM for Network selection in Heterogeneous Wireless Networks. Global Journal of Computer Science and Technology, 11(9), 19−24.

Şenkayas H., Öztürk M., & Sezen G. (2010). Lojistik tedarikçilerin seçiminde analitik hiyerarşi süreci (ahp) yöntemi: mondial şirketinde bir uygulama. Pamukkale Universitesi Sosyal Bilimler Enstitusü Dergisi, (5), 161-175.

Şenyiğit, E., & Ünal, Z. (2019). BWM-MOPA Yöntemi ile en iyi RFID sisteminin belirlenmesi. Avrupa Bilim ve Teknoloji Dergisi Özel Sayı, 9-14.

Stanujkic, D., Karabasevic, D., & Sava, C. (2018). An application of the PIPRECIA and WS PLP methods for evaluating website quality in hotel industry. Quaestus, 12, 190-198.

Stanujkic, D., Zavadskas, E.K., Karabasevic, D., Smarandache, F., & Turskis, Z. (2017). The Use of The Pivot Pairwise Relative Criteria Importance Assessment Method for Determining The Weights of Criteria. Romanian Journal of Economic Forecasting, 20(4), 116-133.

Suzuki, (2021). Teknik Özellikler. https://www.suzuki.com.tr/tr/motosiklet/gsxs-1000.html (21.01.2021).

Taka, M., Raygor, S. P., Purohit, R., & Parashar, V. (2017). Selection of tool and work piece combination using Multiple Attribute Decision Making Methods for Computer Numerical Control turning operation. Materials Today: Proceedings 4. 5th International Conference of Materials Processing and Characterization (ICMPC 2016), 1199–1208.

Wongnitipat, S. & Gerdsri, N. (2010). Supplier Evaluation and Selection: A Case Example of Motoraycle Manufacturing Company. KMUTT Research and Development Journal, 34 (1), 59-75.

Yamaha, (2021). Teknik Özellikler. https://www.yamaha-motor.eu/tr/tr/products/motorcycles/supersport/yzf-r1/techspecs/ ( 21.01.2021)

Yarlıkaş, S., & Can, Z. V. (2020). Yeşil tedarik zinciri yönetimini etkileyen faktörlerin önem sıralamalarının SWARA ve Copeland yöntemleri ile belirlenmesi. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 14(3), 899-924.

Yontar, İ. G., & Aras, B. B. (2018). Motosiklet Kullanımının Kentsel Trafik Güvenliği Üzerine Etkisi: İzmir Üzerine Bir Araştırma. Bitlis Eren Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 7(2), 611-640.