- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: OzcanCelik - A ...
Tamaño: 857.5Kb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Özcan, Sami es_ES
dc.contributor.author Çelik, Ali Kemal es_ES
dc.coverage.spatial east=35.243322; north=38.963745; name=Turquia es_ES
dc.date.accessioned 2021-07-29T09:51:03Z
dc.date.available 2021-07-29T09:51:03Z
dc.date.issued 2021-07-29
dc.identifier.uri http://hdl.handle.net/10251/170824
dc.description.abstract [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. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof International Journal of Production Management and Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Machine selection es_ES
dc.subject Decision making es_ES
dc.subject TOPSIS es_ES
dc.subject Grey relational analysis es_ES
dc.subject COPRAS es_ES
dc.title A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/ijpme.2021.14734
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation 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.2021.14734 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/ijpme.2021.14734 es_ES
dc.description.upvformatpinicio 81 es_ES
dc.description.upvformatpfin 92 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2340-4876
dc.relation.pasarela OJS\14734 es_ES
dc.description.references Ahmed, 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/10.1155/2019/2850480 es_ES
dc.description.references Aloini, D., Dulmin, R., Mininno, V. (2014). A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Systems with Applications, 41(5), 2157-2165 es_ES
dc.description.references https://doi.org/10.1016/j.eswa.2013.09.014 es_ES
dc.description.references Alpay, 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-2018-0053 es_ES
dc.description.references Antucheviciene, 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_.pdf es_ES
dc.description.references Ayağ, 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/s10845-005-6635-1 es_ES
dc.description.references Belton, V., Stewart, T.J. (2002). Multiple criteria decision analysis: An integrated approach. Berlin: Kluwer Academic Publishers. es_ES
dc.description.references https://doi.org/10.1007/978-1-4615-1495-4 es_ES
dc.description.references Camcı, 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-01-2018-0017 es_ES
dc.description.references Çakır, 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/s10845-015-1189-3 es_ES
dc.description.references Çelen, 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.2014.10 es_ES
dc.description.references Chandan, 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/10.1002/9780813804033 es_ES
dc.description.references Chatterjee, 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.073.23 es_ES
dc.description.references Clarke, 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)90530-6 es_ES
dc.description.references Datta, 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-05-2013-0008 es_ES
dc.description.references Deng, 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)00069-6 es_ES
dc.description.references Doğan, 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-z es_ES
dc.description.references Ertuğrul, 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_65 es_ES
dc.description.references Ertuğrul, 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/23647/251887 es_ES
dc.description.references FAO. (2019a). Dairy Market Review. FAO Publishing, Rome.FAO. (2019b). Food Outlook - Biannual Report on Global Food Markets. FAO Publishing, Rome. es_ES
dc.description.references 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.2017.08.098 es_ES
dc.description.references Feng, 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-X es_ES
dc.description.references Guo, 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/10.1155/2016/1812094 es_ES
dc.description.references Gurmeric, 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/s11947-012-0972-9 es_ES
dc.description.references Hwang, C.L., Yoon, K. (1980). Multiple attribute decision making methods and applications: A state-of-the-art survey. New York: Springer-Verlag. es_ES
dc.description.references 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.2021.13323 es_ES
dc.description.references Kabak, 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-20141123105333 es_ES
dc.description.references Kang, 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. es_ES
dc.description.references https://doi.org/10.1007/s10845-010-0448-6 es_ES
dc.description.references Karaman, 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.2013-7111 es_ES
dc.description.references Karim, 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-4-1-2 es_ES
dc.description.references Kumru, 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-y es_ES
dc.description.references Nguyen, 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.2013.10.039 es_ES
dc.description.references OECD/FAO. (2019). OECD-FAO Agricultural Outlook 2019-2028. OECD Publishing, Paris. es_ES
dc.description.references 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.2008.02.045 es_ES
dc.description.references Özceylan, 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-151895 es_ES
dc.description.references Özdağoğlu, 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.2017321605 es_ES
dc.description.references Özgen, 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/10.1080/18756891.2011.9727802 es_ES
dc.description.references Ozturk, 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.2014.05.001 es_ES
dc.description.references Pang, 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/s10845-011-0551-3 es_ES
dc.description.references Paramasivam, 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/s00170-010-2997-4 es_ES
dc.description.references Pavličić, 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=0354-02430102251P es_ES
dc.description.references Samanta, 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.2002.111.2.136 es_ES
dc.description.references Seçme, 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.2009.03.013 es_ES
dc.description.references Sharma, 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/10.1080/10426914.2011.551910 es_ES
dc.description.references Shih, 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.2006.03.023 es_ES
dc.description.references Stanujkic, 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/sjm8-3774 es_ES
dc.description.references Štirbanović, 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.2019.04.014 es_ES
dc.description.references Sun, 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/s11135-013-9854-0 es_ES
dc.description.references Taha, 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/s00170-011-3323-5 es_ES
dc.description.references Temiz, 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.2017.07.201 es_ES
dc.description.references Tosun, 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-y es_ES
dc.description.references Uğur, 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.369058 es_ES
dc.description.references Ulubeyli, 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/10.3846/1392-3730.2009.15.369-376 es_ES
dc.description.references Vafaei, 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.2018.090667 es_ES
dc.description.references Vatansever, 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.2018.090667 es_ES
dc.description.references https://doi.org/10.1504/IJIDS.2018.090667 es_ES
dc.description.references Wang, 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.2008.11.035 es_ES
dc.description.references Wu, 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.2010.10.046 es_ES
dc.description.references Wu, 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/s10479-015-2067-2 es_ES
dc.description.references Wu, 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.2016.02.007 es_ES
dc.description.references Yazdani-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.2012.02.021 es_ES
dc.description.references Yılmaz, 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-problem es_ES
dc.description.references Yılmaz, 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.2011.03.043 es_ES
dc.description.references Zavadskas, 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)00091-2 es_ES
dc.description.references Zhang, 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.2010.02.007 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem