A novel HGEDM method for evaluating 3-axis CNC machines in green environment under uncertainty

Abstract: In the face of digitization in manufacturing industries, the judicious evaluation and selection of cutting-edge CNC machines play a pivotal role in achieving production-grade precision, accuracy and manufacturing agility. The evaluation of 3-axes CNC machines incorporates most sought-after subjective and objective criteria having significant relative weights and green impacts. This research paper presents a novel heterogeneous expert based decision making (HGEDM) framework incorporating a diversified combination of experts having distinct impact factors. The experts’ impact factors so calculated impart significant contributions in computing weighted aggregated performance ratings of the alternatives. To establish the effectiveness of the suggested approach, three practical selection problems are illustrated. The calculated findings are validated with few well-established approaches demonstrating the realistic nature of the suggested methodology. To assess the stability and robustness of the proposed approach, a sensitivity analysis is performed. Besides, Spearman’s rank correlation measure demonstrates that the ranks obtained using the proposed approach are highly close to those derived from several existing methods. Furthermore, both Pearson correlation coefficient and Sample correlation coefficient measures show a strong association between the proposed approach and existing ones. Therefore, the proposed HGEDM approach is considered to be a consistent and effective tool for supporting optimal selection.

How to cite this paper

 Dutta, S., Bairagi, B & Dey, B. (2026). A novel HGEDM method for evaluating 3-axis CNC machines in green environment under uncertainty.Uncertain Supply Chain Management, 14(3), 185-212.

Refrences

Aghdaie, M.H., Zolfani, S.H., & Zavadskas, E.K. (2013). Decision making in machine tool selection: An integrated approach with SWARA and COPRAS-G methods, Engineering economics, 24(1), 5-17. Alberti, M., Ciurana, J., Rodriguez, C., & Ozel, T. (2009), Design of a decision support system for machine tool selection based on machine characteristics and performance tests, Journal of Intelligent Manufacturing, 22, 263–277. Athawale, V.M., & Chakrabarty, S. (2010, January), A TOPSIS Method-based Approach to Machine Tool Selection. In 2010: International Conference on Industrial Engineering and Operations Managemen (IEOM 2010), IEOM society. Ayag, Z. (2007). A hybrid approach to machine-tool selection through AHP and simulation, International Journal of Production Research, 45, 2029-2050. Ayag, Z., & Ozdemir, R.G. (2005). A Fuzzy AHP Approach to Evaluating Machine Tool Alternatives, Journal of Intelligent Manufacturing, 17, 179–190. Ayag, Z., & Ozdemir, R.G. (2012). Evaluating machine tool alternatives through modified TOPSIS and alpha-cut based fuzzy ANP, International Journal of Production Economics, 140, 630-636. Balaji.C.M., Gurumurthy.A., Kodali.R., (2009), Selection of a machine tool for FMS using ELECTRE III — a case study, IEEE International Conference on Automation Science and Engineering, 22-25 August,2009, Bangalore, India Bologa, O., Breaz, R.E., Racz, S.G., & Crenganis, M. (2016). Decision-making tool for moving from 3-axes to 5-axes CNC machine-tool. In 2016: Information Technology and Quantitative Management (ITQM 2016) (pp.184-192). Procedia Computer Science. Bologa, O., Breaz, R.E., Racz, S.G., & Crenganis, M. (2016). Using the Analytic Hierarchy Process (AHP) in evaluating the decision of moving to a manufacturing process based upon continuous 5 axes CNC machine-tools. In 2016: Information Technology and Quantitative Management (ITQM 2016) ( pp.683-689). Procedia Computer Science. Breaz, R.E., Bologa, O., Racz, S.G., & Crenganis, M. (2019). Selecting between CNC turning enters using a combined AHP and fuzzy approach. In 2019: 7th International Conference on Information Technology and Quantitative Management (ITQM 2019) (pp.290–297). Procedia Computer Science. Camci, A., Temur, G.T., & Beskese, A. (2018). CNC router selection for SMEs in woodwork manufacturing using hesitant fuzzy AHP method, Journal of Enterprise Information Management, 1-36. Chakraborty,S & Boral,S (2017), A developed case-based reasoning system for machine tool selection, Benchmarking An International Journal, 24(5). Cimren, E., Catay, B., & Budak.E. (2006), Development of a machine tool selection system using AHP, The International Journal of Advanced Manufacturing Technology, 35, 363–376. Dagdeviren, M.(2008). Decision making in equipment selection: an integrated approach with AHP and PROMETHEE, Journal of Intelligent Manufacturing, 19, 397–406. Ding, Z., Jiang, Z., Zhang, H., Cai, W., & Liy, Y. (2018). An integrated decision-making method for selecting machine tool guideways considering remanufacturability, International Journal of Computer Integrated Manufacturing , 33, 686-700. DU, Y., Zheng, Y., Wu, G., & Tang, Y. (2019). Decision-making method of heavy-duty machine tool remanufacturing based on AHP-entropy weight and extension theory, Journal of Cleaner Production, 252. Durán O., & Aguilo J. (2008). Computer-aided machine-tool selection based on a fuzzy-AHP approach. Expert Systems with Applications, 34, 1787-1794. Dutta, S., Bairagi, B., & Dey, B. (2024), A DE Novo multi criteria heterogeneous group decision making approach for green performance assessment of CNC machine tools, Decision Science Letters, 13, 499–524 İç, T.Y., & Yurdkul, M. (2009). Development of a decision support system for machining center selection, Expert Systems with Applications, 36, 3505-3513. Ic,T. Y., Yurdakul, M., & Erasian, E. (2012). Development of a component-based machining center selection model using AHP, International Journal of Production Research, 50, 6489-6498. Ilangkumaran, M., Sasirekha, V., Anojkumar, L., Boopathi Raja, M. (2012), Machine tool selection using AHP and VIKOR methodologies under fuzzy environment, International Journal of Modeling in Operations Management, 2(4), 409–436. Kabak.M. & Dağdeviren.M., (2017), A hybrid approach based on ANP and grey relational analysis for machine selection, Technical Gazette, 24(Suppl. 1), 109-118 Li, H., Wang, W., & Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR, Applied Soft Computing, 91. Mondal, S., Kundu, C., Chatterjee, P., & Chakraborty, S. (2017), CNC Machine tool selection using Data Envelopment Analysis. In 2017: International conference on advances in science and Technology (ICAST), pp. 295-301, MCKV institute of engineering, India Nguyen, H.T., Dawal, S.Z., Nukman, Y., Aoyama, H., & Case, K. (2015). An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation, Plos one, 10(9), 1-24. Nguyen. H., Dawal.S.Z., 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, 3078-3090. Onut, S., Kara, S.S., & Efendigil, T. (2008). A hybrid fuzzy MCDM approach to machine tool selection, Journal of Intelligent Manufacturing, 19, 443–453. Ozgen, A., Tuzkaya, G., Tuzkaya, U.R, & Ozgen, D. (2011). A Multi-Criteria Decision Making Approach for Machine Tool Selection Problem in a Fuzzy Environment, International Journal of Computational Intelligence Systems, 4, 431-445. Patil, R.B., & Kothavale, S.B. (2020). Criticality Analysis of CNC Turning Center Using Analytic Hierarchy Process, Reliability and Risk Assessment in Engineering, 61-76. Prasad, K., & Chakraborty, S. (2015). Development of a QFD-based expert sytem for CNC turning center selection, Journal of industrial engineering International, 11, 575-594 Sahin, Y., & Aydemir, E. (2022). A Comprehensive Solution Approach for CNC Machine Tool Selection problem. Informatica, 33(1), 81–108. Sahu, A.K., Datta, S., & Mahapatra, S.S. (2015). GDMP for CNC machine tool selection with a compromise ranking method using generalized fuzzy circumstances. International Journal of Computer Aided Engineering and Technology, 7(1), 92-108. Samdevi, A., Jain, V., & Chan. F.T. (2011). An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis, International Journal of Production Research, 50, 3211-3221. Sun.B., Chen.H., Du. L., & Fang. Y. (2008). Machine Tools Selection Technology for Networked Manufacturing, Proceedings of the IEEE: International Symposium on Intelligent Information Technology and Application, IITA '08, 20-22 December 2008, Shanghai, China, pp. 530–534. Taha, Z., & Rostam, S. (2011). A fuzzy AHP–ANN-based decision support system for machine tool selection in a flexible manufacturing cell, The International Journal of Advanced Manufacturing Technology, 57, 719-733. Taha, Z., & Rostam, S. (2011). A hybrid fuzzy AHP-PROMETHEE decision support system for machine tool selection in flexible manufacturing cell, Journal of Intelligent Manufacturing, 23, 2137–2149. Tho, N. H., Dawal, S. Z. M., Yusoff, N., Tahriri, F., & Aoyama, H. (2013), Selecting a CNC machine tool using the intuitionistic fuzzy TOPSIS approach for FMC, Applied Mechanics and Materials, 315, 196-205. Vafadar, A., Rad, M.T., & Hayward, K. (2019). An integrated model to use drilling modular machine tools, The International Journal of Advanced Manufacturing Technology, 102, 2387–2397. Vafadar, A., Hayward, K., & Tolouei-Rad, M. (2017). Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand, Journal of Manufacturing Systems, 45, 58-69. Wang, T.Y., Shaw, C.F., & Chen, Y.L. (2010). Machine selection in flexible manufacturing cell: A fuzzy multiple attribute decision-making approach. International Journal of Production Research, 38, 2079-2097. Wu., 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. Yang, Z., Guo, J., Hailong, T. H., Chen, C., Zhu, Y., & Liu, J. (2021). Weakness Ranking Method for Subsystems of Heavy Duty Machine Tools Based on FMECA Information, Chinese Journal of Mechanical Engineering, 34(17). Yurdalul, M., & ĺç, Y.T. (2009), Analysis of the benefit generated by using fuzzy numbers in a TOPSIS model developed for machine tool selection problems. Journal of Materials Processing Technology, 209, 310-317. Yusuf, Iç., Mustafa Yurdakul, M. (2022). A new long term (strategic) ranking model for machining center selection decisions based on the review of machining center structural components using triangular fuzzy numbers, Decision Analytics Journal, 4, 100081.