The m-polar fuzzy set ELECTRE-I with revised Simos’ and AHP weight calculation methods for selection of non-traditional machining processes
Keywords:M-polar, ELECTRE-I, AHP, TOPSIS, Simos’.
Using improvements to the recently published m-polar fuzzy set (mFS) elimination and choice translating reality-I (ELECTRE-I) approach for calculating criteria weights, the selection of a Non-Traditional Machining (NTM) process problem from the industry is solved in this research. The criteria weights for the m-polar fuzzy ELECTRE-I method are evaluated using the Analytical Hierarchy Process (AHP) approach and the Revised Simos' method. For the ELECTRE family's criteria weight calculations, the Simos’ approach has been revised. Many researchers calculated the weight of the criteria in the selection of the NTM process using the AHP approach. Problems with both physical and intangible properties can be solved using the m-polar fuzzy ELECTRE-I approach. Additionally, it has the ability to solve MCDM issues with more variables. The improved Simos' technique is used in this work because it incorporates user choices for the criteria, or user voting for the criterion. Using expert assistance, the AHP technique prioritizes the criterion based on pair-by-pair comparisons of the criteria. The AHP approach makes compromises between the criteria. The ultimate selection of the process based on the needed aim is affected by both tangible and intangible features in the NTM selection dilemma. The impact of criteria weight techniques on the choice of the NTM process is examined using a single dimensional sensitivity analysis. AHP approach is proven to be less stable for criteria weight variation than the improved Simos' weight calculation method. The updated Simos' method, which takes into account user preferences, performs better for the m-polar fuzzy ELECTRE-I algorithm than the AHP weight calculation method.
Adeel, A., Akram, M., Ahmed, I., & Nazar, K. (2019). Novel m-polar fuzzy linguistic ELECTRE-I method for group decision-making. Symmetry, 11(4), 1–26.
Adeel, A., Akram, M., & Koam, A. N. A. (2019). Multi-Criteria Decision-Making under mHF ELECTRE-I and HmF ELECTRE-I. Energies, 12(9), 1–30.
Akram, M., Waseem, N., & Liu, P. (2019). Novel Approach in Decision Making with m–Polar Fuzzy ELECTRE-I. International Journal of Fuzzy Systems, 21(4), 1117–1129.
Asghari, F., Amidian, A. A., Muhammadi, J., & Rabiee, H. R. (2010). A fuzzy ELECTRE approach for evaluating mobile payment business models. Proceedings - 2010 International Conference on Management of e-Commerce and e-Government, ICMeCG 2010, 351–355.
Aytac, E., Tus, I. A., & Kundakci, N. (2011). Fuzzy ELECTRE I Method for Evaluating Catering Firm Alternatives. Ege Academic Review, 11(2011), 125–134.
Boral, S., & Chakraborty, S. (2016). A case-based reasoning approach for non-traditional machining processes selection. Advances in Production Engineering And Management, 11(4), 311–323.
Chakladar, N. Das, Das, R., & Chakraborty, S. (2009). A digraph-based expert system for non-traditional machining processes selection. International Journal of Advanced Manufacturing Technology, 43(3–4), 226–237.
Chakladar, N. D., & Chakraborty, S. (2008). A combined TOPSIS-AHP-method-based approach for non-traditional machining processes selection. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(12), 1613–1623.
Chakrabarti, S., Mitra, S., & Bhattacharyya, B. (2007). Development of an management information system as knowledge base model for machining process characterisation. International Journal of Advanced Manufacturing Technology, 34(11–12), 1088–1097.
Chakraborty, S. (2011). Applications of the MOORA method for decision making in manufacturing environment. International Journal of Advanced Manufacturing Technology, 54(9–12), 1155–1166.
Chakraborty, S., & Dey, S. (2006). Design of an analytic-hierarchy-process-based expert system for non-traditional machining process selection. International Journal of Advanced Manufacturing Technology, 31(5–6), 490–500.
Chakraborty, S., & Dey, S. (2007). QFD-based expert system for non-traditional machining processes selection. Expert Systems with Applications, 32(4), 1208–1217.
Chatterjee, P., Mondal, S., Boral, S., Banerjee, A., & Chakraborty, S. (2017). A novel hybrid method for non-traditional machining process selection using factor relationship and multi-attribute border approximation method. Facta Universitatis, Series: Mechanical Engineering, 15(3), 439–456.
Chen, J., Li, S., Ma, S., & Wang, X. (2014). M-Polar fuzzy sets: An extension of bipolar fuzzy sets. Scientific World Journal, 2014.
Cogun, C. (1993). Computer-aided system for selection of nontraditional machining operations. Computers in Industry, 22(2), 169–179.
Das, S., & Chakraborty, S. (2011). Selection of non-traditional machining processes using analytic network process. Journal of Manufacturing Systems, 30(1), 41–53.
Edison Chandrasselan, R., Jehadeesan, R., & Raajenthiren, M. (2008). Web-based knowledge base system for selection of non-traditional machining processes. Malaysian Journal of Computer Science, 21(1), 45–56.
Figueira, J., & Roy, B. (2002). Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. European Journal of Operational Research, 139(2), 317–326.
Hatami-Marbini, A., Tavana, M., Moradi, M., & Kangi, F. (2013). A fuzzy group Electre method for safety and health assessment in hazardous waste recycling facilities. Safety Science, 51(1), 414–426.
Jagtap, M., & Karande, P. (2021). Effect of normalization methods on rank performance in single valued m-polar fuzzy ELECTRE-I algorithm. Materials Today: Proceedings.
Jagtap, M., Karande, P., & Athawale, V. M. (2021). Rank Assessment of Robots Using m-Polar Fuzzy ELECTRE-I Algorithm. Proceedings of the International Conference on Industrial Engineering and Operations Management, 246–255.
Karande, P., & Chakraborty, S. (2012). Application of PROMETHEE-GAIA method for non-traditional machining processes selection. Management Science Letters, 2(6), 2049–2060.
Karande, P., Zavadskas, E. K., & Chakraborty, S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7(3), 399–422.
Kaya, T., & Kahraman, C. (2011). An integrated fuzzy AHP-ELECTRE methodology for environmental impact assessment. Expert Systems with Applications, 38(7), 8553–8562.
Khandekar, A. V., & Chakraborty, S. (2016). Application of fuzzy axiomatic design principles for selection of non-traditional machining processes. International Journal of Advanced Manufacturing Technology, 83(1–4), 529–543.
Mousseau, V., Roy, B., & Paris-dauphine, U. (2005). Electre Methods. In: Multiple Criteria Decision Analysis: State of the Art Surveys. International Series in Operations Research & Management Science, vol 78. (133-162), Springer, New York.
Prasad, K., & Chakraborty, S. (2014). A decision-making model for non-traditional machining processes selection. Decision Science Letters, 3(4), 467–478.
Prasad, K., & Chakraborty, S. (2018). A decision guidance framework for non-traditional machining processes selection. Ain Shams Engineering Journal, 9(2), 203–214.
Recherche, R. O., & Roy, O. B. (1968). Classement et choix en présence de points de vue multiples. Revue Française D’Automatique, D’Informatique Et De recherche opérationnelle. Recherche opérationnelle, 2, 57-75.
Rouyendegh, B. D., & Erkan, T. E. (2013). An application of the Fuzzy ELECTRE method for academic staff selection. Human Factors and Ergonomics In Manufacturing, 23(2), 107–115.
Roy, M. K., Ray, A., & Pradhan, B. B. (2014). Non-traditional machining process selection using integrated fuzzy AHP and QFD techniques: a customer perspective. Production and Manufacturing Research, 2(1), 530–549.
Saaty, T. L. (2002). Decision making with the Analytic Hierarchy Process. Scientia Iranica, 9(3), 215–229.
Sadhu, A., & Chakraborty, S. (2011). Non-traditional machining processes selection using data envelopment analysis (DEA). Expert Systems with Applications, 38(7), 8770–8781.
Temuçin, T., Tozan, H., Vayvay, Ö., Harničárová, M., & Valíček, J. (2014). A fuzzy based decision model for nontraditional machining process selection. International Journal of Advanced Manufacturing Technology, 70(9–12), 2275–2282.
Ustinovichius, L., & Simanaviciene, R. (2010). The sensitivity analysis for cooperative decision by TOPSIS method. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6240 LNCS, 89–96.
Yurdakul, M., & Çoǧun, C. (2003). Development of a multi-attribute selection procedure for non-traditional machining processes. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 217(7), 993–1009.
Zadeh, L. . (1965). Fuzzy Set Theory. Information and Control, 8, 338–353.