Methods to find strength of job competition among candidates under single-valued neutrosophic soft model

Sundas Shahzadi; Areen Rasool; Gustavo Santos-García; Sundas Shahzadi; Areen Rasool; Gustavo Santos-García

doi:10.3934/mbe.2023214

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 3: 4609-4642. doi: 10.3934/mbe.2023214

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Research article

Methods to find strength of job competition among candidates under single-valued neutrosophic soft model

1.
Department of Mathematics, Division of Science and Technology, University of Education, Lahore, Pakistan
2.
IME, Universidad de Salamanca, 37007 Salamanca, Spain

Received: 21 August 2022 Revised: 06 October 2022 Accepted: 11 October 2022 Published: 27 December 2022

Neutrosophic soft set theory is one of the most developed interdisciplinary research areas, with multiple applications in various fields such as computational intelligence, applied mathematics, social networks, and decision science. In this research article, we introduce the powerful framework of single-valued neutrosophic soft competition graphs by integrating the powerful technique of single-valued neutrosophic soft set with competition graph. For dealing with different levels of competitive relationships among objects in the presence of parametrization, the novel concepts are defined which include single-valued neutrosophic soft k-competition graphs and p-competition single-valued neutrosophic soft graphs. Several energetic consequences are presented to obtain strong edges of the above-referred graphs. The significance of these novel concepts is investigated through application in professional competition and also an algorithm is developed to address this decision-making problem.
- single-valued neutrosophic set,
- soft set,
- competition graphs,
- single-valued neutrosophic digraph,
- decision-making
Citation: Sundas Shahzadi, Areen Rasool, Gustavo Santos-García. Methods to find strength of job competition among candidates under single-valued neutrosophic soft model[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4609-4642. doi: 10.3934/mbe.2023214

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Abstract

Neutrosophic soft set theory is one of the most developed interdisciplinary research areas, with multiple applications in various fields such as computational intelligence, applied mathematics, social networks, and decision science. In this research article, we introduce the powerful framework of single-valued neutrosophic soft competition graphs by integrating the powerful technique of single-valued neutrosophic soft set with competition graph. For dealing with different levels of competitive relationships among objects in the presence of parametrization, the novel concepts are defined which include single-valued neutrosophic soft k-competition graphs and p-competition single-valued neutrosophic soft graphs. Several energetic consequences are presented to obtain strong edges of the above-referred graphs. The significance of these novel concepts is investigated through application in professional competition and also an algorithm is developed to address this decision-making problem.

References

[1]	J. E. Cohen, Interval graphs and food webs: a finding and a problem, Document 17696-PR, RAND Corporation, Santa Monica, 1968.
[2]	J. R. Lundgren, Food webs, competition graphs, competition-common enemy graphs, and niche graphs, in Applications of Combinatorics and Graph Theory to the Biological and Social Sciences, (1989), 221–243. https://doi.org/10.1007/978-1-4684-6381-1_9
[3]	S. R. Kim, T. A. McKee, F. R. McMorris, F. S. Roberts, p-Competition graphs, Linear Algebra Appl., 217 (1995), 167–178. https://doi.org/10.1016/0024-3795(94)00060-Q doi: 10.1016/0024-3795(94)00060-Q
[4]	R. C. Brigham, F. R. McMorris, R. P. Vitray, Tolerance competition graphs, Linear Algebra Appl., 217 (1995), 41–52. https://doi.org/10.1016/0024-3795(94)00059-M doi: 10.1016/0024-3795(94)00059-M
[5]	H. H. Cho, S. R. Kim, Y. Nam, The $m$-step competition graph of a digraph, Discrete Appl. Math., 105 (2000), 115–127. https://doi.org/10.1016/S0166-218X(00)00214-6 doi: 10.1016/S0166-218X(00)00214-6
[6]	L. A. Zadeh, Fuzzy sets, Inf. Control, 8 (1965), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X doi: 10.1016/S0019-9958(65)90241-X
[7]	A. Kaufmann, Introduction à la théorie des sous-ensembles flous: À l'usage des ingénieurs (Fuzzy Sets Theory), Tome Ⅲ, Paris, French, 1975.
[8]	L. A. Zadeh, Similarity relations and fuzzy orderings, Inf. Sci., 3 (1971), 177–200. https://doi.org/10.1016/S0020-0255(71)80005-1 doi: 10.1016/S0020-0255(71)80005-1
[9]	A. Rosenfeld, Fuzzy graphs, in Fuzzy Sets and their Applications to Cognitive and Decision Processes, Academic Press, (1975), 77–95. https://doi.org/10.1016/B978-0-12-775260-0.50008-6
[10]	P. Bhattacharya, Some remarks on fuzzy graphs, Pattern Recognit. Lett., 6 (1987), 297–302. https://doi.org/10.1016/0167-8655(87)90012-2 doi: 10.1016/0167-8655(87)90012-2
[11]	J. N. Mordeson, P. S. Nair, Fuzzy Graphs and Fuzzy Hhypergraphs, Physica, Springer Verlag, Heidelberg, 2012.
[12]	S. Samanta, M. Pal, Fuzzy k-competition graphs and p-competition fuzzy graphs, Fuzzy Inf. Eng., 5 (2013), 191–204. https://doi.org/10.1007/s12543-013-0140-6 doi: 10.1007/s12543-013-0140-6
[13]	M. Sarwar, M. Akram, Novel concepts of bipolar fuzzy competition graphs, J. Appl. Math. Comput., 54 (2017), 511–547. https://doi.org/10.1007/s12190-016-1021-z doi: 10.1007/s12190-016-1021-z
[14]	R. Sahu, S. R. Dash, S. Das, Career selection of students using hybridized distance measure based on picture fuzzy set and rough set theory, Decis. Mak. Appl. Manag. Eng., 4 (2021), 104–126. https://doi.org/10.31181/dmame2104104s doi: 10.31181/dmame2104104s
[15]	A. Ashraf, K. Ullah, A. Hussain, M. Bari, Interval-valued picture fuzzy Maclaurin symmetric mean operator with application in multiple attribute decision-making, Rep. Mech. Eng., 3 (2022), 301–317. https://doi.org/10.31181/rme20020042022a doi: 10.31181/rme20020042022a
[16]	S. Shahzadi, A. Rasool, M. Sarwar, M. Akram, A framework of decision making based on bipolar fuzzy competition hypergraphs, J. Intell. Fuzzy Syst., 41 (2021), 1319–1339. https://doi.org/10.3233/JIFS-210216 doi: 10.3233/JIFS-210216
[17]	K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets Syst., 20 (1986), 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3 doi: 10.1016/S0165-0114(86)80034-3
[18]	S. Sahoo, M. Pal, Intuitionistic fuzzy competition graphs, J. Appl. Math. Comput., 52 (2016), 37–57. https://doi.org/10.1007/s12190-015-0928-0 doi: 10.1007/s12190-015-0928-0
[19]	F. Smarandache, Neutrosophy, Neutrosophic Probability, Set and Logic, American Research Press, Rehoboth, USA, 105 (1998).
[20]	M. Akram, G. Shahzadi, Operations on single-valued neutrosophic graphs, J. Uncertain Syst., 11 (2017), 1–26.
[21]	M. Akram, A. Luqman, Certain network models using single-valued neutrosophic directed hypergraphs, J. Intell. Fuzzy Syst., 33 (2017), 575–588. https://doi.org/10.3233/JIFS-162347 doi: 10.3233/JIFS-162347
[22]	M. Akram, S. Siddique, Neutrosophic competition graphs with applications, J. Intell. Fuzzy Syst., 33 (2017), 921–935. https://doi.org/10.3233/JIFS-162207 doi: 10.3233/JIFS-162207
[23]	C. Karamasa, E. Demir, S. Memis, S. Korucuk, Weighting the factors affecting logistics outsourcing, Decis. Mak. Appl. Manag. Eng., 4 (2020), 19–32.
[24]	D. Molodtsov, Soft set theory-first results, Comput. Math. Appl., 37 (1999), 19–31. https://doi.org/10.1016/S0898-1221(99)00056-5 doi: 10.1016/S0898-1221(99)00056-5
[25]	P. K. Maji, Neutrosophic soft set, Ann. Fuzzy Math. Inf., 5 (2013), 157–168.
[26]	B. Ahmad, A. Kharal, On fuzzy soft sets, Adv. Fuzzy Syst., 2009 (2009), 1–6. https://doi.org/10.1155/2009/586507 doi: 10.1155/2009/586507
[27]	F. Feng, C. Li, B. Davvaz, M. I. Ali, Soft sets combined with fuzzy sets and rough sets: A tentative approach, Soft Comput., 14 (2010), 899–911. https://doi.org/10.1007/s00500-009-0465-6 doi: 10.1007/s00500-009-0465-6
[28]	I. Deli, S. Broumi, Neutrosophic soft matrices and NSM-decision making, J. Intell. Fuzzy Syst., 28 (2015), 2233–2241. https://doi.org/10.3233/IFS-141505 doi: 10.3233/IFS-141505
[29]	I. Deli, S. Broumi, Neutrosophic soft relations and some properties, Ann. Fuzzy Math. Inf., 9 (2015), 169–182.
[30]	M. Akram, S. Shahzadi, A. Rasool, M. Sarwar, Decision-making methods based on fuzzy soft competition hypergraphs, Complex Intell. Syst., 8 (2022), 2325–2348. https://doi.org/10.1007/s40747-022-00646-4 doi: 10.1007/s40747-022-00646-4
[31]	H. S. Nawaz, M. Akram, Oligopolistic competition among the wireless internet service providers of Malaysia using fuzzy soft graphs, J. Appl. Math. Comput., 67 (2021), 855–890. https://doi.org/10.1007/s12190-021-01514-z doi: 10.1007/s12190-021-01514-z
[32]	H. S. Nawaz, M. Akram, J. C. R. Alcantud, An algorithm to compute the strength of competing interactions in the Bering Sea based on Pythagorean fuzzy hypergraphs, Neural Comput. Appl., 34 (2022), 1099–1121. https://doi.org/10.1007/s00521-021-06414-8 doi: 10.1007/s00521-021-06414-8
[33]	M. Akram, S. Shahzadi, Neutrosophic soft graphs with application, J. Intell. Fuzzy Syst., 32 (2017), 841–858. https://doi.org/10.3233/JIFS-16090 doi: 10.3233/JIFS-16090
[34]	M. Akram, H. S. Nawaz, Implementation of single-valued neutrosophic soft hypergraphs on human nervous system. Artif. Intell. Rev., 2022 (2022). https://doi.org/10.1007/s10462-022-10200-w doi: 10.1007/s10462-022-10200-w
[35]	M. Akram, H. S. Nawaz, Algorithms for the computation of regular single-valued neutrosophic soft hypergraphs applied to supranational asian bodies, J. Appl. Math. Comput., 2022 (2022). https://doi.org/10.1007/s12190-022-01714-1 doi: 10.1007/s12190-022-01714-1
[36]	H. Wang, F. Smarandache, Y. Zhang, R. Sunderraman, Single Valued Neutrosophic Sets, Infinite Study, Coimbatore, India, 2010.

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