Machine Selection Using Fuzzy Choquet Integral Methodology: Case Study in a Textile Company

Abstract

Selecting the most suitable equipment for production is a complex decision-making problem due to the presence of multiple and often conflicting criteria. Decision makers must evaluate several alternatives and criteria simultaneously, which makes it difficult to identify the optimal choice. Although Many Multi-Criteria Decision-Making (MCDM) approaches have been proposed in the literature, most assume that the criterion values are precise and clearly define an assumption that rarely holds in real-world applications. To address this limitation, this study employs the fuzzy Choquet Integral method, which incorporates fuzzy logic to handle the uncertainty and subjectivity inherent in expert evaluations. Through this approach, decision makers can express their assessments using linguistic terms instead of precise numerical values, thereby reducing potential bias in subjective judgments. The proposed method was applied to a real-world industrial sewing machine selection problem in a textile company. Six alternative machines were evaluated across six criteria, using linguistic assessments provided by the production manager. The fuzzy Choquet Integral model was then used to aggregate these evaluations and determine the most suitable alternative. The results show that the proposed approach effectively models interaction among criteria and provides a realistic decision-support framework for equipment selection problems.

Keywords

• Multi Criteria Decision Making
• Fuzzy Choquet Integral
• Sewing Machine Selection
• Textile Company

References

1. [1]. Jones, R. M. (2002). The apparel industry. Oxford: Blackwell Science.
2. [2]. Syduzzaman, M., & Golder, A. S. (2015). Apparel analysis for layout planning in sewing section. International Journal of Current Engineering and Technology, 5(3), 1736–1742.
3. [3]. 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. Tekstil ve Konfeksiyon, 25(1), 80–85.
4. [4]. Ulutaş, A. (2017). Sewing machine selection for a textile workshop by using EDAS method. Journal of Business Research, 9(2), 169–183.
5. [5]. Ilgın, M. A. (2019). Sewing machine selection using linear physical programming. Tekstil ve Konfeksiyon, 29(4), 300–304.
6. [6]. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
7. [7]. Miç, P., & Antmen, Z. F. (2019). A healthcare facility location selection problem with fuzzy TOPSIS method for a regional hospital. Avrupa Bilim ve Teknoloji Dergisi, (16), 750–757.
8. [8]. Tsai, H. H., & Lu, I. Y. (2006). The evaluation of service quality using generalized Choquet Integral. Information Sciences, 176(6), 640–663.

9. [9]. Gurbuz, T., Alptekin, S. E., & Alptekin, G. I. (2012). A hybrid MCDM methodology for ERP selection problem with interacting criteria. Decision Support Systems, 54(1), 206–214.
10. [10]. Zhang, M., Zhou, D., & Zhou, P. (2014). A real option model for renewable energy policy evaluation with application to solar PV power generation in China. Renewable and Sustainable Energy Reviews, 40, 944-955.
11. [11]. Pasrija, V., Kumar, S., & Srivastava, P. R. (2012). Assessment of software quality: Choquet Integral approach. Procedia Technology, 6, 153–162.
12. [12]. Cebi, S. (2013). A quality evaluation model for the design quality of online shopping websites. Electronic Commerce Research and Applications, 12(2), 124–135.
13. [13]. Meng, F., & Zhang, Q. (2014). Induced continuous Choquet Integral operators and their application to group decision making. Computers & Industrial Engineering, 68, 42–53.
14. [14]. Wu, Y., Geng, S., Zhang, H., & Gao, M. (2014). Decision framework of solar thermal power plant site selection based on linguistic Choquet operator. Applied Energy, 136, 303–311.
15. [15]. Wu, J., Chen, F., Nie, C., & Zhang, Q. (2013). Intuitionistic fuzzy-valued Choquet Integral and its application in multicriteria decision making. Information Sciences, 222, 509–527.
16. [16]. Büyüközkan, G., & Göçer, F. (2019). Smart medical device selection based on intuitionistic fuzzy Choquet Integral. International Journal of Intelligent Systems, 34(8), 2019–2040.
17. [17]. Büyüközkan, G., Göçer, F., & Uztürk Baran, Z. (2021). A novel Pythagorean fuzzy set integrated Choquet Integral approach for vertical farming technology assessment. Computers and Electronics in Agriculture, 190, 106410.
18. [18]. Yazıcı, M. Y., & Yıldız, A. (2017). Location selection for underground natural gas storage using Choquet Integral. Energy Sources, Part B: Economics, Planning, and Policy, 12(6), 520–528.
19. [19]. Beg, I., & Rashid, T. (2014). Multi-criteria of bike purchasing using fuzzy Choquet Integral. Economic Modelling, 37, 517–525.
20. [20]. Rizvi, M. Z. (2024). Green supplier selection: Harnessing fuzzy Choquet Integral operator for environmental considerations. International Interdisciplinary Business-Economics Advancement Journal (IIBA-J), 3(1), 35–47.
21. [21]. Akpınar, M. E. (2021). Unmanned aerial vehicle selection using fuzzy choquet integral. Journal of Aeronautics and Space Technologies, 14(2), 119-126.
22. [22]. Akpınar, M. E., & Ilgın, M. A. (2021). Location selection for a Covid-19 field hospital using fuzzy choquet integral method. Gümüşhane Üniversitesi Sosyal Bilimler Dergisi, 12(3), 1095-1109.
23. [23]. Fortemps, P., & Roubens, M. (1996). Ranking and defuzzification methods based on area compensation. Fuzzy Sets and Systems, 82, 319–330.
24. [24]. Auephanwiriyakul, S., Keller, J., & Gader, P. (2002). Generalized Choquet fuzzy Integral fusion. Information Fusion, 3, 69–85.
25. [25]. Vu, H. Q., Beliakov, G., & Li, G. (2013). A Choquet Integral toolbox and its application in customers preference analysis. In Data mining applications with R (pp. 247–272). Elsevier.
26. [26]. Delgado, M., Herrera, F., & Herrera, V. E. (1998). Combining numerical and linguistic information in group decision making. Information Sciences, 107, 177–194.
27. [27]. Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision making: Methods and applications. Berlin: Springer-Verlag.
28. [28]. Kahraman, C., Öztayşi, B., Sarı, İ. U., & Turanoğlu, E. (2016). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. In C. Kahraman & B. Öztayşi (Eds.), Fuzzy multi-criteria decision making: Theory and applications with recent developments, 53–83.