Refrences:
1.Ramanathan, R. ABC inventory classi_cation with multiple-criteria using weighted linear optimization", Comput. Oper. Res., 33, pp. 695-700 (2006).
2. Bhattacharya, A., Sarkar, B., and Mukherjee, S.K. Distance-based consensus method for ABC analysis", Int. J. Prod. Res., 45, pp. 3405-3420 (2007).
3. Hwang, C.L. and Yoon, K., Multiple Attributes Decision Making Method and Application, Springer, Berlin (1981).
4. Zadeh, L. The concept of a linguistic variable and its application to approximate reasoning", Part 1, Inform. Sciences, 8, pp. 199-249 (1975). 5. Hameed, I.A. Using Gaussian membership functions for improving the reliability and robustness of stuA. dents' evaluation systems", Expert. Syst. Appl., 38, pp. 7135-7142 (2011). 6. Cherif, H. and Ladhari, T. A novel multi-criteria inventory classi_cation approach: arti_cial bee colony algorithm with VIKOR method", In: Czach_orski T., Gelenbe E., Grochla K., Lent R. (Eds.), Computer and Information Sciences, Communications in Computer and Information Science, 659, Springer, Cham (2016). 7. Isen, E. and Boran, S. A novel approach based on combining ANFIS, genetic algorithm and fuzzy cmeans methods for multiple criteria inventory classi_- cation", Arab. J. Sci. Eng., 43, pp. 3229-3239 (2018). 8. Lopez-Soto, D., Angel-Belloa, F., Yacoutb, S., and Alvarez, A. A multi-start algorithm to design a multi-class classi_er for a multi-criteria ABC inventory classi_cation problem", Expert. Syst. Appl., 81, pp. 12- 21 (2017). 9. Zhou, P. and Fan, L. A note on multi-criteria ABC inventory classi_cation using weighted linear optimization", Eur. J. Oper. Res., 182, pp. 1488-1491 (2007). 10. Ng, W.L. A simple classi_er for multiple criteria ABC analysis", Eur. J. Oper. Res., 177, pp. 344-353 (2007). 11. Hadi-Vencheh, A. An improvement to multiple criteria ABC inventory classi_cation", Eur. J. Oper. Res., 201, pp. 962-965 (2010). 12. Torabi, S.A., Hate_, S.M., and Saleck Pay, B. ABC inventory classi_cation in the presence of both quantitative and qualitative criteria", Comput. Ind. Eng., 63, pp. 530-537 (2012). 13. Hate_, S.M., Torabi, S.A., and Bagheri, P. Multicriteria ABC inventory classi_cation with mixed quantitative and qualitative criteria", Int. J. Prod. Econ., 52, pp. 776-786 (2014). 14. Kaabi, H. and Jabeur K. A new hybrid weighted optimization model for multi criteria ABC inventory classi_cation", In Proceedings of the Second International Afro-European Conference for Industrial Advancement, Springer, pp. 261-270 (2016). 15. Cohen, M.A. and Ernst, R. Multi-item classi_cation and generic inventory stock control policies", Prod. Inv. Manage. J., 29, pp. 6-8 (1988). 16. Lei, Q., Chen, J., and Zhou, Q. Multiple criteria inventory classi_cation based on principle components analysis and neural network", Adv. Neural. Network., 3498, pp. 1058-1063 (2005). 17. Ghorabaee, M.K., Zavadskas, E.K., and Zenonas Turskis, L.O. Multi-criteria inventory classi_cation using a new method of evaluation based on distance from average solution (EDAS)", Informatica., 26, pp. 435-451 (2015). 18. Raja, A.M.L., Ai, J., and Astanti, R.D. A clustering classi_cation of spare parts for improving inventory policies", IOP Conf. Series: Materials Science and Engineering, 114, Kuala Lumpur, Malaysia (2016). 19. Chen,Y., Li, K.W., Kilgour, D.M., and Hipel, K.W. A case-based distance model for multiple criteria ABC analysis", Comput. Oper. Res., 35, pp. 776-796 (2008). 20. Ma, L.C. A two-phase case-based distance approach for multiple-group classi_cation problems", Comput. Ind. Eng., 63, pp. 89-97 (2012). 21. Jiang, H. A multi-attribute classi_cation method on fresh agricultural products", J. Comput., 9, pp. 2443- 2448 (2014). 22. Arikan, F. and Citak, S. Multiple criteria inventory classi_cation in an electronics _rm", Int. J. Info. Tech. Dec. Mak., 16, pp. 315-331 (2017). 23. Dhar, A.R. and Sarkar, B. Application of the MOORA method for multi-criteria inventory classi_cation", Conference: 1st Frontiers in Optimization: Theory and Applications, Heritage Institute of Technology Kolkata (2017). 24. Douissa, M.R. and Jabeur, K. A new model for multicriteria ABC inventory classi_cation: PROAFTN method", Procedia. Comput. Sci., 96, pp. 550-559 (2016). 25. Lajili, I., Ladhari, T., and Babai, Z. Adaptive machine learning classi_ers for the class imbalance problem in ABC inventory classi_cation", 6th International Conference on Information Systems, Logistics and Supply Chain. ILS Conference, Bordeaux, France (2016). 26. Hu, Q., Chakhar, S., Siraj, S., and Labib, A. Spare parts classi_cation in industrial manufacturing using the dominance-based rough set approach", Eur. J. Oper. Res., 262, pp. 1136-1163 (2017). 27. Lolli, F., Ishizaka, A., Gamberini, R., Balugani, E., and Rimini, B. Decision trees for supervised multicriteria inventory classi_cation", Procedia. Manuf., 11, pp. 1871-1881 (2017). 28. Hadi-Vencheh, A. and Mohamadghasemi, A. A fuzzy AHP-DEA approach for multiple criteria ABC inventory classi_cation", Expert. Syst. Appl., 38, pp. 3346- 3352 (2011). 29. Kabir, G. and Sumi, R.S. Integrating fuzzy Delphi with fuzzy analytic hierarchy process for multiple criteria inventory classi_cation", J. Eng. Proj. Prod. Manag., 3, pp. 22-34 (2013). 30. Kabir, G. and Hasin, M.A.A. Multi-criteria inventory classi_cation through integration of fuzzy analytic hierarchy process and arti_cial neural network", Int. J. Ind. Syst. Eng., 14, pp. 74-103 (2013). 31. Lolli, F., Ishizaka, A., and Gamberini, R. New AHPbased approaches for multi-criteria inventory classi_- cation", Int. J. Prod. Econ., 156, pp. 62-74 (2014). 32. Douissa, M.R. and Jabeur, K. A new multi-criteria ABC inventory classi_cation model based on a simpli _ed electre III method and the continuous variable 3006 A. Mohamadghasemi et al./Scientia Iranica, Transactions E: Industrial Engineering 26 (2019) 2988{3006 neighborhood search", 6th International Conference on Information Systems, Logistics and Supply Chain, Bordeaux, France (2016). 33. Mendel, J.M., John, R.I., and Feilong, L. Interval type-2 fuzzy logic systems made simple", IEEE Trans. Fuzzy. Syst., 14, pp. 808-821 (2006). 34. Mendel, J.M., Uncertain Rule-based Fuzzy Logic Systems: Introduction and New Directions, Prentice Hall, Upper Saddle River, NJ (2001). 35. Chen, T.Y. An integrated approach for assessing criterion importance with interval type-2 fuzzy sets and signed distances", J. Chinese. Inst. Indus. Eng., 28, pp. 553-572 (2011). 36. Chen, T.Y. Multiple criteria group decision-making with generalized interval-valued fuzzy numbers based on signed distances and incomplete weights", Appl. Math. Modell., 36, pp. 3029-3052 (2012). 37. Tahayori, H., Tettamanzi, A., and Antoni, G.D. Approximated type-2 fuzzy set operations", In Proceedings of FUZZ-IEEE 2006, Vancouver, Canada, pp. 9042-9049 (2006). 38. Moore, R.E. Methods and applications of interval analysis", Philadelphia, SIAM (1979). 39. Buckley, J.J. Ranking alternatives using fuzzy numbers", Fuzzy. Set. Syst., 15, pp. 21-31 (1985). 40. Rashid, T., Beg, I., and Husnine, S.M. Robot selection by using generalized interval-valued fuzzy numbers with TOPSS", Appl. Soft. Comput., 21, pp. 462-468 (2014). 41. Shipley, M.F., Korvin, D.K., and Obit, R. A decision making model for multi-attribute problems incorporating uncertainty and bias measures", Comp. Oper. Res., 18, pp. 335-342 (1991).