A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey

[EN] The paper aims to compare the results of the selection/choice of cream separators by using multi-criteria decision-making methods in an integrated manner for an enterprise with a dairy processing capacity of 80 to 100 tons per day operating in the Turkish food sector. A total of 7 alternative products and 7 criteria for milk processing were determined. Criterion weights were calculated using entropy method and then integrated into TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions), GRA (Grey Relational Analysis) and COPRAS (Complex Proportional Assessment) methods. Sensitivity analyses were carried out on the results obtained from the three methods to check for their reliability. At the end of the study, similar alternative and appropriate results were found from the TOPSIS and COPRAS methods. However, different alternative but appropriate or suitable results were obtained from the GRA method. Sensitivity analysis of the three methods showed that all the methods used were valid. In the review of available and related literature, very few studies on machine selection in the dairy and food sector in general were found. For this reason, it is thought that the study will contribute to the decision-making process of companies in the dairy sector in their choice of machinery selections. As far as is known, this paper is the first attempt in extant literature to compare in an integrated manner the results of TOPSIS, COPRAS and GRA methods considered in the study.Özcan, S.; Çelik, AK. (2021). A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey. International Journal of Production Management and Engineering. 9(2):81-92. https://doi.org/10.4995/ijpme.2021.14734OJS819292Ahmed, M., Qureshi, M.N., Mallick, J., Kahla, N.B. (2019). Selection of sustainable supplementary concrete materials using OSM-AHP-TOPSIS approach. Advances in Materials Science and Engineering, 2019, 1-12. https://doi.org/10.1155/2019/2850480Aloini, D., Dulmin, R., Mininno, V. (2014). A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Systems with Applications, 41(5), 2157-2165https://doi.org/10.1016/j.eswa.2013.09.014Alpay, S., Ihpar, M. (2018). Equipment selection based on two different fuzzy multi criteria decision making methods: Fuzzy TOPSIS and fuzzy VIKOR. Open Geosciences, 10(1), 661-677. https://doi.org/10.1515/geo-2018-0053Antucheviciene, J., Zavadskas, E.K., Zakarevičius, A. (2012). Ranking redevelopment decisions of derelict buildings and analysis of ranking results. Economic Computation and Economic Cybernetics Studies and Research, 46(2), 37-63. Retrieved June 08, 2020 from http://www.ecocyb.ase.ro/22012/Edmundas%20ZAVADSKAS%20_DA_.pdfAyağ, Z., Özdemir, R.G. (2006). A fuzzy AHP approach to evaluating machine tool alternatives. Journal of Intelligent Manufacturing, 17(2), 179-190. https://doi.org/10.1007/s10845-005-6635-1Belton, V., Stewart, T.J. (2002). Multiple criteria decision analysis: An integrated approach. Berlin: Kluwer Academic Publishers.https://doi.org/10.1007/978-1-4615-1495-4Camcı, A., Temur, G.T., Beşkese, A. (2018). CNC router selection for SMEs in woodwork manufacturing using hesitant fuzzy AHP method. Journal of Enterprise Information Management, 31(4), 529-549. https://doi.org/10.1108/JEIM-01-2018-0017Çakır, S. (2018). An integrated approach to machine selection problem using fuzzy SMART-fuzzy weighted axiomatic design. Journal of Intelligent Manufacturing, 29(7), 1433-1445. https://doi.org/10.1007/s10845-015-1189-3Çelen, A. (2014). Comparative analysis of normalization procedures in TOPSIS method: With an application to Turkish deposit banking market. Informatica, 25(2), 185-208. https://doi.org/10.15388/Informatica.2014.10Chandan, R.C. (2008). Dairy Processing and Quality Assurance: An Overview. Ramesh C. Chandan, Arun Kilara, Nagendra Shah (Eds.), In Dairy Processing and Quality Assurance (pp. 1-40). New Jersey: Wiley-Blackwell. https://doi.org/10.1002/9780813804033Chatterjee, P., Chakraborty, S. (2014). Investigating the effect of normalization norms in flexible manfacturing sytem selection using Multi-Criteria Decision-Making methods. Journal of Engineering Science and Technology Review, 7(3), 141-150. https://doi.org/10.25103/jestr.073.23Clarke, M.P., Denby, B., Schofield, D. (1990). Decision making tools for surface mine equipment selection. Mining Science and Technology, 10(3), 323-335. https://doi.org/10.1016/0167-9031(90)90530-6Datta, S., Sahu, N., Mahapatra, S. (2013). Robot selection based on grey-MULTIMOORA approach. Grey Systems: Theory and Application, 3(2), 201-232. https://doi.org/10.1108/GS-05-2013-0008Deng, H., Yeh, C.H., Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers and Operations Research, 27(10), 963-973. https://doi.org/10.1016/S0305-0548(99)00069-6Doğan, M., Aslan, D., Aktar, T., Sarac, M.G. (2016). A methodology to evaluate the sensory properties of instant hot chocolate beverage with different fat contents: multi-criteria decision-making techniques approach. European Food Research and Technology, 242(6), 953-966. https://doi.org/10.1007/s00217-015-2602-zErtuğrul, İ., Güneş, M. (2007). Fuzzy multi-criteria decision making method for machine selection. P. Melin, O. Castillo, E.G. Ramirez, J. Kacprzyk and W. Pedrycz (Eds.), In Analysis and Design of Intelligent Systems Using Soft Computing Techniques (pp. 638-648). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-540-72432-2_65Ertuğrul, İ., Öztaş, T. (2015). The application of sewing machine selection with the multi-objective optimization on the basis of ratio analysis method (MOORA) in apparel sector. Textile and Apparel, 25(1), 80-85. Retrieved May 17, 2020 from https://dergipark.org.tr/tr/pub/tekstilvekonfeksiyon/issue/23647/251887FAO. (2019a). Dairy Market Review. FAO Publishing, Rome.FAO. (2019b). Food Outlook - Biannual Report on Global Food Markets. FAO Publishing, Rome.Feizabadi, A., Doolabi, M.S., Sadrnezhaad, S.K., Zafarani, H.R., Doolabi, D.S. (2017). MCDM selection of pulse parameters for best tribological performance of Cr-Al2O3 nano-composite co-deposited from trivalent chromium bath. Journal of Alloys and Compounds, 727, 286-296. https://doi.org/10.1016/j.jallcom.2017.08.098Feng, C.M., Wang, R.T. (2000). Performance evaluation for airlines including the consideration of financial ratios. Journal of Air Transport Management, 6(3), 133-142. https://doi.org/10.1016/S0969-6997(00)00003-XGuo, X., Sun, Z. (2016). A novel evaluation approach for tourist choice of destination based on grey relation analysis. Scientific Programming, 2016, 1-10. https://doi.org/10.1155/2016/1812094Gurmeric, V.E., Dogan, M., Toker, O.S., Senyigit, E., Ersoz, N.B. (2013). Application of different multi-criteria decision techniques to determine optimum flavour of prebiotic pudding based on sensory analyses. Food and Bioprocess Technology, 6(10), 2844-2859. https://doi.org/10.1007/s11947-012-0972-9Hwang, C.L., Yoon, K. (1980). Multiple attribute decision making methods and applications: A state-of-the-art survey. New York: Springer-Verlag.Jahan, A., Yazdani, M., Edwards, K.L. (2021). TOPSIS-RTCID for range target-based criteria and interval data. International Journal of Production Management and Engineering, 9(1), 1-14. https://doi.org/10.4995/ijpme.2021.13323Kabak, M., Dağdeviren, M. (2017). A hybrid approach based on ANP and Grey Relational Analysis for machine selection. Technical Gazette, 24(Supplement 1), 109-118. https://doi.org/10.17559/TV-20141123105333Kang, H.Y., Lee, A.H.I., Yang, C.Y. (2012). A fuzzy ANP model for supplier selection as applied to IC packaging. Journal of Intelligent Manufacturing, 23(5), 1477-1488.https://doi.org/10.1007/s10845-010-0448-6Karaman, S.,Toker, Ö.S., Yüksel, F., Çam, M., Kayacier, A., Dogan, M. (2014). Physicochemical, bioactive, and sensory properties of persimmon-based ice cream: Technique for order preference by similarity to ideal solution to determine optimum concentration. Journal of Dairy Science, 97(1), 97-110. https://doi.org/10.3168/jds.2013-7111Karim, R., Karmaker, C.L. (2016). Machine selection by AHP and TOPSIS methods. American Journal of Industrial Engineering, 4(1), 7-13. https://doi.org/10.12691/ajie-4-1-2Kumru, M., Kumru, P.Y. (2015). A fuzzy ANP model for the selection of 3D coordinate-measuring machine. Journal of Intelligent Manufacturing, 26(5), 999-1010. https://doi.org/10.1007/s10845-014-0882-yNguyen, H.T., Dawal, S. Z. Md., Nukman, Y., Aoyama, H. (2014). A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes. Expert Systems with Applications, 41(6), 3078-3090. https://doi.org/10.1016/j.eswa.2013.10.039OECD/FAO. (2019). OECD-FAO Agricultural Outlook 2019-2028. OECD Publishing, Paris.Önüt, S., Kara, S.S., Işik, E. (2009). Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company. Expert Systems with Applications, 36(2), 3887-3895. https://doi.org/10.1016/j.eswa.2008.02.045Özceylan, E., Kabak, M., Dağdeviren, M. (2016). A fuzzy-based decision making procedure for machine selection problem. Journal of Intelligent and Fuzzy Systems, 30(3), 1841-1856. https://doi.org/10.3233/IFS-151895Özdağoğlu, A., Yakut, E., Bahar, S. (2017). Machine selection in a dairy product company with Entropy and SAW methods integration. Faculty of Economics and Administrative Sciences Journal, 32(1), 341-359. https://doi.org/10.24988/deuiibf.2017321605Özgen, A., Tuzkaya, G., Tuzkaya, U.R., Özgen, D. (2011). A multi-criteria decision making approach for machine tool selection problem in a fuzzy environment. International Journal of Computational Intelligence Systems, 4(4), 431-445. https://doi.org/10.1080/18756891.2011.9727802Ozturk, G., Dogan, M., Toker, O.S. (2014). Physicochemical, functional and sensory properties of mellorine enriched with different vegetable juices and TOPSIS approach to determine optimum juice concentration. Food Bioscience, 7, 45-55. https://doi.org/10.1016/j.fbio.2014.05.001Pang, B., Bai, S. (2013). An integrated fuzzy synthetic evaluation approach for supplier selection based on analytic network process. Journal of Intelligent Manufacturing, 23(5), 163-174. https://doi.org/10.1007/s10845-011-0551-3Paramasivam, V., Senthil, V., Ramasamy, N.R. (2011). Decision making in equipment selection: an integrated approach with digraph and matrix approach, AHP and ANP. The International Journal of Advanced Manufacturing Technology, 54(9-12), 1233-1244. https://doi.org/10.1007/s00170-010-2997-4Pavličić, D.M. (2001). Normalisation affects the results of MADM methods. Yugoslav Journal of Operations Research, 11(2), 251-265. Retrieved May 6, 2020 from http://scindeks.ceon.rs/article.aspx?artid=0354-02430102251PSamanta, B., Sarkar, B., Mukherjee, S.K. (2002). Selection of opencast mining equipment by a multi-criteria decision-making process. Mining Technology, 111(2), 136-142. https://doi.org/10.1179/mnt.2002.111.2.136Seçme, N.Y., Bayrakdaroğlu, A., Kahraman, C. (2009). Fuzzy performance evaluation in Turkish Banking Sector using Analytic Hierarchy Process and TOPSIS. Expert Systems with Applications, 36(9), 11699-11709. https://doi.org/10.1016/j.eswa.2009.03.013Sharma, A., Yadava, V. (2011). Optimization of cut quality characteristics during nd:yag laser straight cutting of ni-based superalloy thin sheet using grey relational analysis with entropy measurement. Materials and Manufacturing Processes, 26(12), 1522-1529. https://doi.org/10.1080/10426914.2011.551910Shih, H. S., Shyur, H.J., Lee, E.S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8), 801-813. https://doi.org/10.1016/j.mcm.2006.03.023Stanujkic, D., Đorđević, B., Đorđević, M. (2013). Comparative analysis of some prominent MCDM methods: A case of ranking Serbian Banks. Serbian Journal of Management, 8(2), 213-241. https://doi.org/10.5937/sjm8-3774Štirbanović, Z., Stanujkić, D., Miljanović, I., Milanović, D. (2019). Application of MCDM methods for flotation machine selection. Minerals Engineering, 137, 140-146. https://doi.org/10.1016/j.mineng.2019.04.014Sun, C.C. (2014). Combining grey relation analysis and entropy model for evaluating the operational performance: An empirical study. Quality and Quantity, 48(3), 1589-1600. https://doi.org/10.1007/s11135-013-9854-0Taha, Z., Rostam, S. (2011). A fuzzy AHP-ANN-based decision support system for machine tool selection in a flexible manufacturing cell. International Journal of Advanced Manufacturing Technology, 57(5-8), 719-733. https://doi.org/10.1007/s00170-011-3323-5Temiz, I., Çalış, G. (2017). Selection of construction equipment by using multi-criteria decision making methods. Procedia Engineering, 196, 286-293. https://doi.org/10.1016/j.proeng.2017.07.201Tosun, N. (2006). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 28(5-6), 450-455. https://doi.org/10.1007/s00170-004-2386-yUğur, L.O. (2017). Application of the VIKOR multi-criteria decision method for construction machine buying. Journal of Polytechnic, 20(4), 879-885. https://doi.org/10.2339/politeknik.369058Ulubeyli, S., Kazaz, A. (2009). A multiple criteria decision-making approach to the selection of concrete pumps. Journal of Civil Engineering and Management, 15(4), 369-376. https://doi.org/10.3846/1392-3730.2009.15.369-376Vafaei, N., Ribeiro, R.A., Camarinha-Matos, L.M. (2018). Data normalisation techniques in decision making: Case study with TOPSIS method. International Journal of Information and Decision Sciences, 10(1), 19-38. https://doi.org/10.1504/IJIDS.2018.090667Vatansever, K., Kazançoğlu, Y. (2014). Integrated usage of fuzzy multi criteria decision making techniques for machine selection problems and an application. International Journal of Business and Social Science, 5(9), 12-24. https://doi.org/10.1504/IJIDS.2018.090667https://doi.org/10.1504/IJIDS.2018.090667Wang, T.C., Lee, H.D. (2009). Developing a fuzzy TOPSIS approach based on subjective weights and objective weights. Expert Systems with Applications, 36(5), 8980-8985. https://doi.org/10.1016/j.eswa.2008.11.035Wu, J., Sun, J., Liang, L., Zha, Y. (2011). Determination of weights for ultimate cross efficiency using Shannon entropy. Expert Systems with Applications, 38(5), 5162-5165. https://doi.org/10.1016/j.eswa.2010.10.046Wu, W., Peng, Y. (2016). Extension of grey relational analysis for facilitating group consensus to oil spill emergency management. Annals of Operations Research, 238(1-2), 615-635. https://doi.org/10.1007/s10479-015-2067-2Wu, Z., Ahmad, J., Xu, J. (2016). A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information. Applied Soft Computing, 42, 314-324. https://doi.org/10.1016/j.asoc.2016.02.007Yazdani-Chamzini, A., Yakhchali, S.H. (2012). Tunnel Boring Machine (TBM) selection using fuzzy multicriteria decision making methods. Tunnelling and Underground Space Technology, 30, 194-204. https://doi.org/10.1016/j.tust.2012.02.021Yılmaz, B., Dağdeviren, M. (2010). Comparative analysis of PROMETHEE and fuzzy PROMETHEE methods in equipment selection problem. Journal of the Faculty of Engineering and Architecture of Gazi University, 25(4), 811-826. Retrieved May 6, 2020 from https://avesis.gazi.edu.tr/yayin/989e528e-9184-4d8e-8970-fccfabbbed73/comparative-analysis-of-promethee-and-fuzzy-promethee-methods-in-equipment-selection-problemYılmaz, B., Dağdeviren, M. (2011). A combined approach for equipment selection: F-PROMETHEE method and zero-one goal programming. Expert Systems with Applications, 38(9), 11641-11650. https://doi.org/10.1016/j.eswa.2011.03.043Zavadskas, E.K., Kaklauskas, A., Banaitis, A., Kvederyte, N. (2004). Housing credit access model: The case for Lithuania. European Journal of Operational Research, 155(2), 335-352. https://doi.org/10.1016/S0377-2217(03)00091-2Zhang, H., Gu, C.L., Gu, L. W., Zhang, Y. (2011). The evaluation of tourism destination competitiveness by TOPSIS and information entropy: A case in the Yangtze River Delta of China. Tourism Management, 32(2), 443-451. https://doi.org/10.1016/j.tourman.2010.02.00

A fuzzy ANP model for the selection of 3D coordinate - measuring machine

Kumru, Mesut (Dogus Author)The analytic network process (ANP) method is normally used to determine the relative weights of a set of evaluation criteria when ranking the competing alternatives in terms of their overall performance. It has the ability to deal with interdependent relationships among the criteria. Since the fuzzy logic approach provides more accuracy on judgments, the fuzzy extension of the ANP method enables the decision-maker to use uncertain human preferences as input information in the decision-making process. The fuzzy ANP enhances the potential of the conventional ANP for dealing with imprecise and vague human comparison judgments. In this work, a fuzzy ANP method is introduced to present a performance analysis on a specific machine tool selection problem. Unlike conventional fuzzy ANP applications, the proposed approach here is to be applied comprehensively for a sophisticated machine selection case in a company. Different from the machine tool selection studies so far done, machine hardware and software are to be discussed together in the selection process. It is used for the selection of a 3D coordinate-measuring machine for a die manufacturing company. The results indicate more accurate and reliable decision making in machine tool selection problem