International Journal of Nonlinear Analysis and Applications
Improved optimality checkpoint for decision making by using the sub-triangular form
Document Type : Research Paper
Author
Presidency of the University of Baghdad, Studies and Planning Department, University of Baghdad, Iraq
Abstract
Decision-making in Operations Research is the main point in various studies in our real-life applications. However, these different studies focus on this topic. One drawback some of their studies are restricted and have not addressed the nature of values in terms of imprecise data (ID). This paper thus deals with two contributions. First, decreasing the total costs by classifying sub-sets of costs. Second, improving the optimality solution by the Hungarian assignment approach. This newly proposed method is called fuzzy sub-Triangular form (FS-TF) under ID. The results obtained are exquisite as compared with previous methods including, robust ranking technique, arithmetic operations, magnitude ranking method and centroid ranking method. This current novelty offers an effective tool to accesses solving the ID to solve assignment problems.
Keywords
Symposium, KSS 2017, Bangkok, Thailand, Springer, 2017.
[2] A.M. Coroiu, Fuzzy methods in decision making process-A particular approach in manufacturing systems, In IOP
Conf. Ser.: Materials Sci. Eng. 95(1) (2015) 012154.
[3] T.G. Crainic, M. Gendreau and J.Y. Potvin, Intelligent freight-transportation systems: Assessment and the
contribution of operations research, Transport. Res. Part C: Emerg. Technol. 17(6) (2009) 541–557.
[4] K. Kalaiarasi, S. Sindhu and M. Arunadevi, Optimization of fuzzy assignment model with triangular fuzzy numbers
using robust ranking technique, Int. J. Innov. Sci. Engg. Technol. 1(3) (2014) 10–15.
[5] M. Khalid, M. Sultana and F. Zaidi, New improved one’s assignment method, Appl. Math. Sci. 8(84) (2014)
4171–4177.
[6] P.S. Kumar, Developing a new approach to solving solid assignment problems under an intuitionistic fuzzy environment, Int. J. Fuzzy Syst. Appl. 9(1) (2020) 1–34.
[7] M. Lee, Y. Xiong, G. Yu and G.Y. Li, Deep neural networks for linear sum assignment problems, IEEE Wireless
Commun. Lett. 7(6) (2018) 962–965.
[8] J. Li, T. Kirubarajan, R. Tharmarasa, D. Brown and K.R. Pattipati, A dual approach to multi-dimensional
assignment problems, J. Global Optim. 81 (2021) 691—716.
[9] M. Mammadova and Z. Jabrayilova, Application of fuzzy optimization method in decision-making for personnel
selection, Intell. Cont. Autom. 5(4) (2014) 190–204.
[10] R.Q. Mary and D. Selvi, Solving fuzzy assignment problem using centroid ranking method, Int. J. Math. Appl.
6(3) (2018) 9–16.
[11] K. Prabakaran and K. Ganesan, The fuzzy Hungarian method for solving intuitionistic fuzzy traveling salesman
problem, J. Phys.: Conf. Ser. 1000(1) (2018) 012008.
[12] A.P. Prakash and M.G. Lakshmi, Sub-trident ranking using fuzzy numbers, Int. J. Math. Appl. 4(2) (2016) 143–
150.
[13] Scientific Workplace, Mathematical Scientific Soft-Ware, https://www.mackichan.com/index.html?products/
dnloadreq55.html mainFrame, (2015).
[14] D. Selvi, R.Q. Mary and G. Velammal, Method for solving fuzzy assignment problem using magnitude ranking
method, Int. J. Appl. Adv. Sci. Res. (2017) 16–20.
[15] A. Simonetto, J. Monteil and C. Gambella, Real-time city-scale ridesharing via linear assignment problems,
Transport. Res. Part C: Emerg. Technol. 101 (2019) 208–232.
[16] J.P. Singh and N.I. Thakur, A novel method to solve assignment problems in a fuzzy environment, Industrial Eng.
Lett. 5(2) (2015) 31–35.
[17] S. Suprasongsin, V.N. Huynh and P. Yenradee, A three-dimensional fuzzy linguistic evaluation model, J. Adv.
Comput. Intell. Intell. Inf. 22(5) (2018) 767–776.
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Volume 13, Issue 1
March 2022Pages 3985-3990
- Receive Date: 06 October 2021
- Revise Date: 10 November 2021
- Accept Date: 29 December 2021