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Article type: Research Article
Authors: Rehman, Noora; * | Ali, Abbasb | Ali Shah, Syed Inayatc | Irfan Ali, Muhammadd | Park, Choonkile
Affiliations: [a] Department of Mathematics and Statistics, Bacha Khan University Charsadda, KPK Pakistan | [b] Department of Mathematics and Statistics, Riphah International University, Islamabad Pakistan | [c] Department of Mathematics, Islamia College University Peshawar, KPK Pakistan | [d] Islamabad Model College for Girls F-6/2 Islamabad Pakistan | [e] Research Institute of Natural Sciences, Hanyang University, Seoul, Republic of Korea
Correspondence: [*] Corresponding author. Noor Rehman, Department of Mathematics and Statistics, Bacha Khan University Charsadda, KPK Pakistan. E-mails: [email protected]; [email protected].
Abstract: The extension of rough set model is a crucial and vast research direction in rough set theory. Meanwhile decision making can be considered as a mental process in which human beings make a choice among several alternatives. However, with the increasing complexity of real decision making problems, the decision makers frequently face the challenge of characterizing their preferences in an uncertain context. In the present paper, we initiate a multi attribute group decision making problem in the presence of multi attribute and multi decision in decision making with preferences. We further present the concept of soft preference relation and soft dominance relation corresponding to decision attribute in the multi criteria and multi decision information system. Further we present the idea of variable precision multi decision soft dominance based rough set model and their applications in solving a multi agent conflict analysis decision problem. The proposed method addresses the limitations of the Pawlak’s model and Sun’s conflict analysis model and thus improve these models. Finally, the results on labor management negotiation problems show that the proposed algorithms are more effective and efficient for feasible consensus strategy when compared with other techniques.
Keywords: Rough set, soft set, preference relation, inclusion degree, soft preference relation
DOI: 10.3233/JIFS-191197
Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 6, pp. 5345-5360, 2019
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