Pruning method for optimal solutions of int–int decision making scheme

Han, Banghe; Geng, Shengling

doi:10.1016/j.ejor.2013.06.044

Cited by 22 publications

(8 citation statements)

References 26 publications

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“…To deal with the foremost necessary origin background of problem containing uncertainty and obscurity, the fuzzy and soft sets were introduced by Zadeh [1] in 1965 and Molodtsov [2] in 1999 respectively. The authors in [3][4][5][6] concentrated on the soft sets and their operations to solve day-to-day life decision making problems which involve the different kinds of uncertainties. Especially, Ç agman et al [7] redefined the operations of Molodtsov's soft sets to make it more functional and then adapted them to the soft matrices in [8].…”

Section: Introductionmentioning

confidence: 99%

The adjustable approaches to multi-criteria group decision making based on inverse fuzzy soft matrices

Petchimuthu¹,

Kamacı

2020

Scientia Iranica

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In this paper, we focus on the matrices representing the inverse fuzzy soft sets over both the universal object set and the universal parameter set. Some basic operations and properties of these inverse fuzzy soft matrices are investigated. Moreover, two adjustable approaches to multi-criteria group decision making (MCGDM), namely inverse fuzzy soft sum-product decision making (IFSSPDM) and inverse fuzzy soft distributive If-difference decision making (IFSDIf-dDM), are proposed. The IFSSPDM approach achieves the optimal choice for the MCGDM problem based on the inverse fuzzy soft structures consisting of multiple-discrete parameter sets and common universal object sets. The objective of IFSDIf-dDM approach is to present a solution for the MCGDM problem based on the inverse fuzzy soft structures consisting of a common universal parameter set and two discrete universal object sets. Thus, the solutions can be obtained using the practicality of inverse fuzzy soft matrices for two different types of decision making problems. Besides, the comparisons are presented showing that the proposed approaches produce more convincing outputs than the current fuzzy soft approaches.

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Section: Introductionmentioning

confidence: 99%

The adjustable approaches to multi-criteria group decision making based on inverse fuzzy soft matrices

Petchimuthu¹,

Kamacı

2020

Scientia Iranica

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“…In 2002, Maji et al [32] put forward that the soft sets can be used to solve decision making problems involving parametric data based on two-valued logic. In the following years, many soft decision algorithms were created and their applications for problems in real life were included [33][34][35][36][37]. Moreover, Eraslan [38] proposed a decision making procedure via classical method of TOPSIS on the soft sets.…”

Section: Introductionmentioning

confidence: 99%

Three-valued soft set and its multi-criteria group decision making via TOPSIS and ELECTRE

Akçetin

Kamacı²

2020

Scientia Iranica

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The purpose of this paper is to introduce a generalization of Molodtsov's approach to soft sets obtained by passing from the classical two-valued logic underlying those sets to a three-valued logic, where the third truth value can usually be interpreted as either non-determined (i.e., between true and false) or unknown. This extension of soft set approach allows for more intuitive and clearer representation of various decision making problems involving incomplete or uncertain information. In other words, it is a useful way to analyze soft set based multi-criteria group decision making problems under the lack of information resulting from the inability to determine the data. In this paper, we introduce the concept of three-valued soft set and its some operations and products. We propose the formulas to calculate all possible choice values for each object in the (weighted) three-valued soft sets, and thus calculate their respective decision values. By modifying the TOPSIS and ELECTRE methods to deal with multi-criteria group decision problems, three-valued soft set based decision making algorithms are constructed. To demonstrate the practicality of these algorithms, we address the examples adapted from the decision problems in real-life. Lastly, some aspects of the efficiency of the proposed algorithms are discussed with computational experiments.

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“…Gunduz, Sonmez and Cakalli in [10] introduced soft open and soft closed mappings, soft homeomorphism concept. An application of soft sets to a decision making problem with the help of the theory of soft sets was studied in [5,12,16,17,22,24].…”

Section: Introductionmentioning

confidence: 99%

The use of soft matrices on soft multisets and their applications in optimal decision process

Erdem

Aras

Sönmez

2018

Filomat

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Pruning method for optimal solutions of int–int decision making scheme

Cited by 22 publications

References 26 publications

The adjustable approaches to multi-criteria group decision making based on inverse fuzzy soft matrices

The adjustable approaches to multi-criteria group decision making based on inverse fuzzy soft matrices

Three-valued soft set and its multi-criteria group decision making via TOPSIS and ELECTRE

The use of soft matrices on soft multisets and their applications in optimal decision process

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