Exploring entropy weighted topsis and bharat approach for multi-criteria decision-making problem

Ghalme, Sachin; Fedai, Yusuf; Thorat, Sandeep

doi:10.22061/jcarme.2026.11116.2464

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	Exploring entropy weighted topsis and bharat approach for multi-criteria decision-making problem
Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 اسفند 1404 اصل مقاله (1.24 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2026.11116.2464
نویسندگان
Sachin Ghalme^* ¹؛ Yusuf Fedai²؛ Sandeep Thorat³
¹Department of Mechanical Engineering, Sandip Institute of Technology and Research Centre, Nashik (MS), India
²Department of Industrial Engineering, Faculty of Engineering, Osmaniye Korkut Ata University, Turkey
³Department of Mechanical Engineering, MIT School of Engineering, MIT Art, Design and Technology University, Loni Kalbhor Pune (MS), India
تاریخ دریافت: 08 مرداد 1403، تاریخ بازنگری: 02 اسفند 1404، تاریخ پذیرش: 06 اسفند 1404
چکیده
Computational models/methods are regularly used by the engineering community to evaluate the optimal set of solutions with respect to the defined performance criteria. In the case of multi-objective optimization problems, the set of Pareto front or optimal solutions needs to be evaluated with a simple and effective search methodology to help the decision maker select the best parameters/factors. In this study an attempt has been made to optimize the content of MWCNTs, cutting speed, and feed rate to minimize delamination factor and thrust force during drilling of MWCNTs reinforced GFRP nano-composite. Entropy-weighted TOPSIS and BHARAT approaches are implemented successfully for evaluating optimal parameters. Experimental result analysis suggests that feed rate being the major contributing factor for affecting delamination factor and thrust force. The optimized levels for MWCNTs, cutting speed and feed rate are 3 (1%), 3 (75 mm/min), and 1 (0.1 mm/rev), respectively, as obtained by both methods. Comparative analysis of entropy-weighted TOPSIS and BHARAT approach has been performed in relation to multi-criteria decision-making problems. The evaluated Pearson's and Spearman's correlation coefficient for both methods are 0.98, suggesting a high correlation between both methods.
کلیدواژه‌ها
Multi-criteria decision making؛ Entropy weighted TOPSIS؛ BHARAT؛ MWCNTs reinforced GFRP nano-composite؛ Machining of Nano-GFRP composite

مراجع
[1] S. Ghalme, A. Mankar and K. Phapale, “Analysis of factors affecting delamination in drilling of GFRP composite”, J. Comput. Appl. Res. Mech. Eng., Vol. 10, No. 2, pp. 281–289,(2021). [2] P. Jagadeesh, M. Puttegowda, Y. G. T. Girijappa, S. M. Rangappa and S. Siengchin, “Carbon fiber-reinforced areca/sisal hybrid composites for railway interior applications: mechanical and morphological properties”, Poly. Comp., Vol.43, pp. 160–172, (2022) [3] M. P. Alves, C. A. C. Junior and S. K. Ha, “Fiber waviness and its effect on the mechanical performance of fiber-reinforced polymer composites: an enhanced review”, Comp. Part A: App. Sci. Manu.,Vol. 149, pp. 1-14, (2021). [4] M. N. Norizan, M. H. Moklis, S. Z. N. Deoman, N. A. Halim, A. Samsuri, I. S. Mohamad, V. F. Knight and N. Abdullha, “Carbonnanotubes: functionalisation and their application in chemical sensors”, RSC Adv., Vol. 10, pp. 43704–43732, (2020). [5] N. M. Nurazzi, M.R.M. Asyraf, A. Khalina, N. Abdullah, F. A. Sabaruddin, S. H. Kamarudin, S. Ahmad, A. M. Mahat, C. L. Lee, H. A. Aisyah, M. N. F. Norrrahim, R. A. Ilyas, M. M. Harussani, M. R. Ishak and S. M. Sapuan, “Fabrication, functionalization, and application of carbon nanotube-reinforced polymer composite: An overview”, Polymers, Vol. 13, No. 7, pp. 1-44,(2021). [6] F. Ceritbinmez and A. Yapici, “The effect of drill point angle and MWCNTs doped in drilling of composite materials”, Acad. Persp. Proce., Vol. 3, pp.180–188, (2020). [7] K. Ramachandran, V. Boopalan, J. C. Bear and R. Subramani, “Multi-walled carbon nanotubes (MWCNTs)-reinforced ceramic nanocomposites for aerospace applications: a review”, J. of Mat. Sci., Vol. 57, pp. 3923–3953, (2022). [8] K. Prashantha, J. Soulestin, M.F. Lacrampe, P. Krawczak, G. Dupin and M. Claes, “Masterbatch-based multi-walled carbon nanotube filled polypropylene nanocomposites: assessment of rheological and mechanical properties”, Comp. Sci. Tech., Vol. 69, No. 11-12, pp. 1756–1763, (2009). [9] C. K. Yang, Y. R. Lee, T. H. Hsieh, T. H. Chen and T. C. Cheng, “Mechanical property of multiwall carbon nanotube reinforced polymer composites”, Pol. Pol. Comp., Vol. 26, No. 1, pp. 99-104, (2018). [10] A. Unuvar, M. Koyunbakan and M. Bagci M, “Optimization and effects of machining parameters on delamination in drilling of pure and Al2O3/SiO2-added GFRP composites”, Int. J. of Adv. Man. Tech., Vol. 119, pp.657–675, (2022). [11] R. V. Rao, V. J. Savsani and D. P. Vakharia, “Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems”, Com.-Aided Des., Vol. 43, No.3, pp.303-315,(2011). [12] Y. K. Chen, S. X. Weng and T. P. Liu, “Teaching–Learning Based Optimization (TLBO) with variable neighborhoodsearch to retail shelf-space Aalocation”, Mathematics, Vol. 8, No. 1296, pp. 1-16, (2020). [13] V. Shukla and M. Bandyopadhyay, “Optimization of input parameters of ANN–driven plasma source through nature-inspired evolutionary algorithms”, Inte. Sys. with App., Vol. 18,pp.1-12,(2023). [14] S.Ghalme, A. Mankar and Y. Bhalerao, “Integrated Taguchi-simulated annealing (SA) approach for analyzing wear behaviour of silicon nitride”, J. of App. Rese. Tech., Vol. 15, No. 6, pp. 624-632, (2017). [15] P. N. K. Phuc and N. L. P. Thao, “Ant colony optimization for multiple pickup and multiple delivery vehicle routing problem with time window and heterogeneous fleets”, Logistics, Vol. 5, No.28, pp.1-13, (2021). [16] A. G. Gad, “Particle swarm optimization algorithm and its applications: A systematic review”, Arch. of Compu. Meth. in Engg., Vol.29, pp. 2531–2561,(2022). [17] S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris and S. M. Mirjalili, “Salpswarm algorithm: A bio-inspired optimizer for engineering design problems”, Adv. in Engg. Soft., Vol.114, pp.163-191,(2017). [18] M. Yazdani and F. Jolai, “Lion optimization algorithm (LOA): a nature-inspired metaheuristic algorithm”, J. of Compu. Des. Engg., Vol. 3, No. 1, pp. 24-36,(2016). [19] S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm”, Adv. in Engg. Soft., Vol.95, pp. 51-67, (2016). [20] E.H. Houssein, A. G. Gad and Y.M. Wazery, “Jaya algorithm and applications: A comprehensive review”, In: Razmjooy, N., Ashourian, M., Foroozandeh, Z. (eds) Metaheuristics and Optimization in Computer and Electrical Engineering. Lecture Notes in Electrical Engineering, Vol. 696. Springer, Cham, (2021). [21] X. X. Xia, Z. Q. Wang and Y. Chen, “Working fluid selection of dual-loop organic Rankine cycle using multi-objective optimization and improved grey relational analysis”, App. Therm. Engg., Vol. 171, pp.1-10, (2020). [22] S. G. Ghalme and P. Karolczak, “Multi-response optimization of drilling parameters for Aluminum metal matrix composite using entropy weighted grey relational analysis”, Adv. Mat. Res., Vol. 1177, pp. 17–29, (2023). [23] S. Verma, V. K. Midha and A. K. Choudhary, “Optimization of parameters for alkali pretreatment on coir fiber for biomass production using TOPSIS”, J. of Nat. Fib., Vol. 19, No. 8, pp. 3038-3050, (2022). [24] N. A. Theeb, H. A. Qdais, F. H. A. Qdais and O. Habibah, “Utilizing AHP-TOPSIS as multi-criteria decision approaches to select the best alternative for waste to energy technology”, Jordan J. of Mech. Ind. Engg., Vol. 16, No. 4, pp. 601-613, (2022). [25] R. V. Rao, “BHARAT: A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-1: Multi-attribute decision-making applications in the industrial environment”, Int. J. of Ind. Engg. Compu., Vol.15, No. 1, pp.13-40,(2024). [26] R. V. Rao, “BHARAT: A simple and effective multi-criteria decision-making method that does not need fuzzy logic, Part-2: Role in multi- and many-objective optimization problems”, Int. J. of Ind. Engg. Compu., Vol.15, No. 1, pp.1-12, (2024). [27] P. K. Kopparthi, V. R. Kundavarapu, V. R. Dasari, V. R. Kaki and B. R. Pathakokila, “Modeling of glass fiber-reinforced composites for optimal mechanical properties using teaching learning based optimization and artificial neural networks”, SN App. Sci., Vol.2, No.131, pp. 1-15, (2020). [28] R. V. Rao, D. P. Rai, J. Ramkumar and J. Balic, “A new multi-objective Jaya algorithm for optimization of modern machining processes”, Adv. in Prod. Engg. Mana., Vol.11, No. 4, pp. 171-186, (2016). [29] S. Chandrasekhar and N. Prasad, “Multi-response optimization of electrochemical machining parameters in the micro-drilling of AA6061-TiB2in situ composites using the Entropy–VIKOR method”, Proce. of the Inst. of Mech. Engrs., Part B: Jr. of Engg. Manu.,Vol. 234, No. 10, pp.1311-1322, (2020). [30] Y. Kuo, T. Yang and G. W. Huang, “The use of a grey-based Taguchi method for optimizing multi-response simulation problems”, Engg. Opt., Vol.40, No. 6, pp. 517-528, (2008). [31] E. Wang, N. Alp, J. Shi, C. Wang, X. Zhang and H. Chen, “Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting”, Energy, Vol. 125, pp.197–210,(2017). [32] A. Mert, “Shannon entropy-based approach for calculating values of WABL parameters”, J. of Taibah Uni. for Sci., Vol.14, No. 1, pp.1100-1109, (2020). [33] X. Ding, X. Chong, Z. Bao, Y. Xue and S. Zhang, “Fuzzy comprehensive assessment method based on the entropy weight method and its application in the water environmental safety evaluation of the Heshangshan drinking water source area, Three Gorges Reservoir Area, China”,Water, Vol. 9, No. 329, pp. 1-15, (2017). [34] R. Kumar, S. Singh, P. S. Bilga, Jatin, J. Singh, S. Singh, M. L. Scutaru and C. I. Pruncu, “Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: A critical review”, J. of Mat. Res. Tech., Vol.10, pp.1471-1492,(2021). [35] C. L. Hwang and K. Yoon, “Multiple attribute decision making methods and applications: A state-of-the-art survey”, Springer: Berlin/Heidelberg, Germany, (1981). [36] M. Rabieha, A. F. Rafsanjania, L. Babaeia and M. Esmaeilib, “Sustainable supplier selection and order allocation: an integrated Delphi method, Fuzzy TOPSIS, and multi-objective programming model”, Scie. Iranica, Vol. 26, No. 4, pp.2524-2540,(2019). [37] R. V. Rao and R. J. Lakshmi, “Ranking of Pareto-optimal solutions and selecting the best solution in multi- and many-objective optimization problems using R-method”, Soft Comp. Lett., Vol. 3(100015), pp. 1-18,(2021). [38] M. Atef, H. Kaid, A. Alsamhan, S. H. Mian and L. Hidri, “Assessment and comparison of various MCDM approaches in the selection of manufacturing process”, Adv. in Mat. Sci. Engg., Article ID 4039253, pp. 1-16, (2020). [39] M. M. Mukaka, “Statistics corner: A guide to appropriate use of correlation coefficient in medical research”, Malawi Med. J., Vol. 24, No. 3, pp.69-71, (2012). [40] J. M. Bland and D. G. Altman, “Measuring agreement in method comparison studies”, Stat. Meth. in Med. Res., Vol. 8,pp.135-60,(1999). [41] J. M. Bland and D. G. Altman,“Statistical methods for assessing agreement between two methods of clinical measurement”,Int. J. of Nur. Stu.,Vol.47,pp.931-
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Exploring entropy weighted topsis and bharat approach for multi-criteria decision-making problem