Next Article in Journal
EWMA Control Chart Using Repetitive Sampling for Monitoring Blood Glucose Levels in Type-II Diabetes Patients
Next Article in Special Issue
Comparative Evaluation of Sustainable Design Based on Step-Wise Weight Assessment Ratio Analysis (SWARA) and Best Worst Method (BWM) Methods: A Perspective on Household Furnishing Materials
Previous Article in Journal / Special Issue
New Analytic Solutions of Queueing System for Shared–Short Lanes at Unsignalized Intersections
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Methods for Multiple-Attribute Group Decision Making with q-Rung Interval-Valued Orthopair Fuzzy Information and Their Applications to the Selection of Green Suppliers

1
School of Business, Sichuan Normal University, Chengdu 610101, China
2
School of Finance, Yunnan University of Finance and Economics, Kunming 650221, China
*
Authors to whom correspondence should be addressed.
Symmetry 2019, 11(1), 56; https://doi.org/10.3390/sym11010056
Submission received: 15 November 2018 / Revised: 24 December 2018 / Accepted: 26 December 2018 / Published: 6 January 2019

Abstract

:
In the practical world, there commonly exist different types of multiple-attribute group decision making (MAGDM) problems with uncertain information. Symmetry among some attributes’ information that is already known and unknown, and symmetry between the pure attribute sets and fuzzy attribute membership sets, can be an effective way to solve this type of MAGDM problem. In this paper, we investigate four forms of information aggregation operators, including the Hamy mean (HM) operator, weighted HM (WHM) operator, dual HM (DHM) operator, and the dual-weighted HM (WDHM) operator with the q-rung interval-valued orthopair fuzzy numbers (q-RIVOFNs). Then, some extended aggregation operators, such as the q-rung interval-valued orthopair fuzzy Hamy mean (q-RIVOFHM) operator; q-rung interval-valued orthopairfuzzy weighted Hamy mean (q-RIVOFWHM) operator; q-rung interval-valued orthopair fuzzy dual Hamy mean (q-RIVOFDHM) operator; and q-rung interval-valued orthopair fuzzy weighted dual Hamy mean (q-RIVOFWDHM) operator are presented, and some of their precious properties are studied in detail. Finally, a real example for green supplier selection in green supply chain management is provided, to demonstrate the proposed approach and to verify its rationality and scientific nature.

1. Introduction

For the indeterminacy of decision makers and decision-making issues, we cannot always give accurate evaluation values for alternatives to select the best project in real multiple-attribute decision making (MADM) problems. To overcome this disadvantage, fuzzy set theory, as defined by Zadeh [1] in 1965, originally used the membership function to describe the estimation results, rather than an exact real number. Atanassov [2,3] presents the intuitionistic fuzzy set (IFS) by considering another measurement index which names a non-membership function. Hereafter, the IFS and its extension has aroused the attention of a large number of scholars since its appearance [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25]. More recently, the Pythagorean fuzzy set (PFS) [26,27] has emerged as a useful tool for describing the indeterminacy of the MADM problems. Zhang and Xu [28] proposed the detailed mathematical expression for PFS and presented the definition of Pythagorean fuzzy numbers (PFNs). Wei and Lu [29] proposed some Maclaurin Symmetric Mean Operators with PFNs. Peng and Yang [30] studied the division and subtraction operations of PFNs. Wei and Lu [31] defined some power aggregation operators with PFNs based on the traditional power aggregation operators [32,33,34,35,36,37]. Beliakov and James [38] presented the average aggregation functions of PFNs. Reformat and Yager [39] studied the collaborative-based recommender system under the Pythagorean fuzzy environment. Gou et al. [40] proposed some desirable properties of the continuous Pythagorean fuzzy number. Wei and Wei [41] defined some similar measures of Pythagorean fuzzy sets, based on cosine functions with traditional similarity measures [42,43,44,45]. Ren et al. [46] applied the Pythagorean fuzzy TODIM model in MADM. Garg [47] combines the Einstein Operations and Pythagorean fuzzy information to propose a new aggregation operator. Zeng et al. [48] provided a Pythagorean fuzzy hybrid method to study MADM. Garg [49] presents a novel accuracy function based on interval-valued Pythagorean fuzzy information for solving MADM problems. Wei et al. [50] propose the Pythagorean hesitant fuzzy Hamacher operators in MADM. Wei and Lu [51] develop the dual hesitant Pythagorean fuzzy Hamacher operators in MADM. Lu et al. [52] develop the hesitant Pythagorean fuzzy Hamacher aggregation operators in MADM.
In addition to this, based on the fundamental theories of IFS and PFS, Yager [53] further defined the q-rung orthopair fuzzy sets (q-ROFSs), in which the sum of the qth power of the degrees of membership and the qth power of the degrees of non-membership is satisfied the condition μ q + ν q 1 . It is clear that the q-ROFSs are more general for IFSs and PFSs, as they are all special cases. Therefore, we can express a wider range of fuzzy information by using q-ROFSs. Liu and Wang [54] develop the q-rung orthopair, fuzzy weighted averaging (q-ROFWA) operator and the q-rung orthopair, fuzzy weighted geometric (q-ROFWG) operator to fuse the evaluation information. Liu and Liu [55] proposes a q-rung orthopair, fuzzy Bonferroni mean (q-ROFBM) aggregation operator, by considering the q-rung orthopair fuzzy information and the Bonferroni mean (BM) operator. Wei et al. [56] combine the q-rung orthopair fuzzy numbers (q-ROFNs) with a generalized Heronian mean (GHM) operator to present some aggregation operators, and applied them into MADM problems. Wei et al. [57] define some q-rung orthopair, fuzzy Maclaurin symmetric mean operators for the potential evaluation of emerging technology commercialization.
Nevertheless, in many practical decision-making problems, for the uncertainty of the decision-making environment and the subjectivity of decision makers (DMs), it is always difficult for DMs to exactly describe their views with a precise number; however, they can be expressed by an interval number within [0, 1]. This denotes that it is necessary to introduce the definition of q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs), of which the degrees of positive membership and negative membership are given by an interval value. This kind of situation is more or less like that encountered in interval-valued, intuitionistic fuzzy environments [58,59]. It should be noted that when the upper and lower limits of the interval values are same, q-RIVOFSs reduce to q-ROFSs, meaning that the latter is a special case of the former.
This research has four main purposes. The first is to develop a comprehensive MAGDM method for selecting the best green supplier with q-RIVOFNs. The second purpose lies in exploring several aggregation operators based on traditional Hamy mean (HM) operators with q-RIVOFNs. The third is to establish an integrated outranking decision-making method by the q-RIVOFWHM (q-RIVOFWDHM) operators. The final purpose is to demonstrate the application, practicality, and effectiveness of the proposed MADM method for selecting the best green supplier.
To further study the q-RIVOFSs, our paper combines the Hamy mean (HM) operator, which considers the relationship between the attribute’s estimation values with q-rung interval-valued orthopair fuzzy numbers to investigate MAGDM problems. For the sake of clarity, the rest of this research is organized as follows. Firstly, we briefly introduce the fundamental theories, such as definition, score, and accuracy functions, and operational laws of the q-ROFSs and q-RIVOFSs in Section 2. Then, based on q-RIVOFSs and Hamy mean (HM) operators, we propose four aggregation operators, including the q-rung interval-valued orthopair, fuzzy Hamy mean (q-RIVOFHM) operator; the q-rung interval-valued orthopair, fuzzy weighted Hamy mean (q-RIVOFWHM) operator; the q-rung interval-valued orthopair, fuzzy dual Hamy mean (q-RIVOFDHM) operator; and the q-rung interval-valued orthopair, fuzzy weighted dual Hamy mean (q-RIVOFWDHM) operator in Section 3. Meanwhile, some important properties of these operators are also studied. Thereafter, the models which apply the proposed aggregation operators to solve MAGDM problems are presented in Section 4, and an illustrative example to select the best green supplier is developed. Some comments are provided to summarize this article in Section 5.

2. Preliminaries

2.1. q-Rung Interval-Valued Orthopair Fuzzy Sets (q-RIVOFSs)

According to the q-rung orthopair fuzzy sets (q-ROFSs) [53] and interval-valued Pythagorean fuzzy sets (IVPFSs) [49], we develop the definition of the q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs).
Definition 1.
Let X be a fixed set. A q-RIVOFS is an object having the form
Q ˜ = { x , ( μ ˜ Q ˜ ( x ) , ν ˜ Q ˜ ( x ) ) | x X }
where μ ˜ Q ˜ ( x ) [ 0 , 1 ] and ν ˜ Q ˜ ( x ) [ 0 , 1 ] are interval numbers, and μ ˜ Q ˜ ( x ) = [ μ Q ˜ L ( x ) , μ Q ˜ R ( x ) ] , ν ˜ Q ˜ ( x ) = [ ν Q ˜ L ( x ) , ν Q ˜ R ( x ) ] with the condition 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 , x X , q 1 . The numbers μ ˜ Q ˜ ( x ) , ν ˜ Q ˜ ( x ) represent, respectively, the function of positive membership degree (PMD) and negative membership degree (NMD) of the element x to Q ˜ . Then, for x X , π ˜ Q ˜ ( x ) = [ π Q ˜ L ( x ) , π Q ˜ R ( x ) ] = [ 1 ( ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q ) q , 1 ( ( μ Q ˜ L ( x ) ) q + ( ν Q ˜ L ( x ) ) q ) q ] denotes the function of the refusal membership degree (RMD) of the element x to Q ˜ .
As a matter of convenience, we called q ˜ = ( [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] ) a q-rung interval-valued orthopair fuzzy number (q-RIVOFN). Let q ˜ = ( [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] ) be a q-RIVOFN, then S ( q ˜ ) = 1 4 [ ( 1 + ( u q ˜ L ) q ( v q ˜ L ) q ) + ( 1 + ( u q ˜ R ) q ( v q ˜ R ) q ) ] and H ( q ˜ ) = ( u q ˜ L ) q + ( u q ˜ R ) q + ( v q ˜ L ) q + ( v q ˜ R ) q 2 are the score and accuracy function of a q-RIVOFN q ˜ .
Definition 2.
Let q ˜ 1 = ( [ u q ˜ 1 L , u q ˜ 1 R ] , [ v q ˜ 1 L , v q ˜ 1 R ] ) and q ˜ 2 = ( [ u q ˜ 2 L , u q ˜ 2 R ] , [ v q ˜ 2 L , v q ˜ 2 R ] ) be two q-RIVOFNs; S ( q ˜ 1 ) and S ( q ˜ 2 ) be the scores of q ˜ 1 and q ˜ 2 , respectively; and let H ( q ˜ 1 ) and H ( q ˜ 2 ) be the accuracy degrees of q ˜ 1 and q ˜ 2 , respectively. Then, if S ( q ˜ 1 ) < S ( q ˜ 2 ) , then q ˜ 1 < q ˜ 2 ; if S ( q ˜ 1 ) = S ( q ˜ 2 ) , then (1) if H ( q ˜ 1 ) = H ( q ˜ 2 ) , then q ˜ 1 = q ˜ 2 ; (2) if H ( q ˜ 1 ) < H ( q ˜ 2 ) , then q ˜ 1 < q ˜ 2 .
Definition 3.
Let q ˜ 1 = ( [ u q ˜ 1 L , u q ˜ 1 R ] , [ v q ˜ 1 L , v q ˜ 1 R ] ) , q ˜ 2 = ( [ u q ˜ 2 L , u q ˜ 2 R ] , [ v q ˜ 2 L , v q ˜ 2 R ] ) , and q ˜ = ( [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] ) be three q-RIVOFNs, and some basic operation rules for them are shown as follows:
( 1 )   q ˜ 1 q ˜ 2 = ( [ ( u q ˜ 1 L ) q + ( u q ˜ 2 L ) q ( u q ˜ 1 L ) q ( u q ˜ 2 L ) q q , ( u q ˜ 1 R ) q + ( u q ˜ 2 R ) q ( u q ˜ 1 R ) q ( u q ˜ 2 R ) q q ] , [ v q ˜ 1 L v q ˜ 2 L , v q ˜ 1 R v q ˜ 2 R ] ) ; ( 2 ) q ˜ 1 q ˜ 2 = ( [ μ q ˜ 1 L v q ˜ 2 L , μ q ˜ 1 R v q ˜ 2 R ] , [ ( v q ˜ 1 L ) q + ( v q ˜ 2 L ) q ( v q ˜ 1 L ) q ( v q ˜ 2 L ) q q , ( v q ˜ 1 R ) q + ( v q ˜ 2 R ) q ( v q ˜ 1 R ) q ( v q ˜ 2 R ) q q ] ) ; ( 3 )   λ q ˜ = ( [ 1 ( 1 ( u q ˜ L ) q ) λ q , 1 ( 1 ( u q ˜ R ) q ) λ q ] , [ ( v q ˜ L ) λ , ( v q ˜ R ) λ ] ) , λ > 0 ; ( 4 )   ( q ˜ ) λ = ( [ ( μ q ˜ L ) λ , ( μ q ˜ R ) λ ] , [ 1 ( 1 ( v q ˜ L ) q ) λ q , 1 ( 1 ( v q ˜ R ) q ) λ q ] ) , λ > 0 ; ( 5 )   q ˜ c = ( [ v q ˜ L , v q ˜ R ] , [ μ q ˜ L , μ q ˜ R ] ) .

2.2. Hamy Mean Operator

Definition 4
[60].The HM operator is defined as follows:
HM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x q ˜ i j ) 1 x C n x
where x is a parameter and x = 1 , 2 , , n , i 1 , i 2 , , i x are x integer values taken from the set { 1 , 2 , , n } of k integer values; C n x denotes the binomial coefficient and C n x = n ! x ! ( n x ) ! .

3. Some Hamy Mean Operators with q-RIVOFNs

3.1. q-RIVOFHM Operator

In this chapter, consider both HM operator and q-RIVOFNs, we propose the q-rung interval-valued orthopair fuzzy Hamy mean (q-RIVOFHM) operator.
Definition 5.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. The q-RIVOFHM operator is
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x q ˜ i j ) 1 x C n x
Theorem 1.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. The fused value by using q-RIVOFHM operator is also a q-RIVOFN, where
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x q ˜ i j ) 1 x C n x = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j L ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j R ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) ) 1 x q ) 1 C n x , ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) ) 1 x q ) 1 C n x ] }
Proof. 
j = 1 x q ˜ i j = { [ j = 1 x u q ˜ j L , j = 1 x u q ˜ j R ] , [ 1 j = 1 x ( 1 ( v q ˜ j L ) q ) q , 1 j = 1 x ( 1 ( v q ˜ j R ) q ) q ] }
Thus,
( j = 1 x q ˜ i j ) 1 x = { [ ( j = 1 x u q ˜ j L ) 1 x , ( j = 1 x u q ˜ j R ) 1 x ] , [ 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) ) 1 x q , 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x q ] }
Thereafter,
1 i 1 < < i x n ( j = 1 x q ˜ i j ) 1 x = { [ 1 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j L ) q x ) q , 1 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j R ) q x ) q ] , [ 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) ) 1 x q , 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x q ] }
Therefore,
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x q ˜ i j ) 1 x C n x = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j L ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j R ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) ) 1 x q ) 1 C n x , ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x q ) 1 C n x ] }
Hence, Equation (4) is kept.
Then, we need to prove that Equation (4) is a q-RIVOFN. We need to prove 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 .
Let
μ Q ˜ R ( x ) = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j R ) q x ) ) 1 C n x q ν Q ˜ R ( x ) = ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x q ) 1 C n x
 □
Proof. 
0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ j R ) q x ) ) 1 C n x + ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x ) ) 1 C n x 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x ) ) 1 C n x + ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) ) 1 x ) ) 1 C n x = 1
So 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 is maintained. □
Example 1.
Let ( [ 0.5 , 0.8 ] , [ 0.4 , 0.5 ] ) , ( [ 0.3 , 0.5 ] , [ 0.6 , 0.7 ] ) , ( [ 0.5 , 0.7 ] , [ 0.2 , 0.3 ] ) and   ( [ 0.4 , 0.8 ] , [ 0.1 , 0.2 ] ) be four q-RIVOFNs, and suppose x   =   2 ,   q   =   3 —then, according to Equation (4), we have
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x q ˜ i j ) 1 x C n x = ( [ 1 ( ( 1 ( 0.5 × 0.3 ) 3 2 ) × ( 1 ( 0.5 × 0.5 ) 3 2 ) × ( 1 ( 0.5 × 0.4 ) 3 2 ) × ( 1 ( 0.3 × 0.4 ) 3 2 ) × ( 1 ( 0.3 × 04 ) 3 2 ) × ( 1 ( 0.5 × 0.4 ) 3 2 ) ) 1 C 4 2 3 , 1 ( ( 1 ( 0.8 × 0.5 ) 3 2 ) × ( 1 ( 0.8 × 0.7 ) 3 2 ) × ( 1 ( 0.8 × 0.8 ) 3 2 ) × ( 1 ( 0.5 × 0.7 ) 3 2 ) × ( 1 ( 0.5 × 0.8 ) 3 2 ) × ( 1 ( 0.7 × 0.8 ) 3 2 ) ) 1 C 4 2 3 ] , [ ( ( ( 1 ( ( 1 0.4 3 ) × ( 1 0.6 3 ) ) 1 2 ) × ( 1 ( ( 1 0.4 3 ) × ( 1 0.2 3 ) ) 1 2 ) × ( 1 ( ( 1 0.4 3 ) × ( 1 0.1 3 ) ) 1 2 ) × ( 1 ( ( 1 0.6 3 ) × ( 1 0.2 3 ) ) 1 2 ) × ( 1 ( ( 1 0.6 3 ) × ( 1 0.1 3 ) ) 1 2 ) × ( 1 ( ( 1 0.2 3 ) × ( 1 0.1 3 ) ) 1 2 ) ) 3 ) 1 C 4 2 , ( ( ( 1 ( ( 1 0.5 3 ) × ( 1 0.7 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.3 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.2 3 ) ) 1 2 ) × ( 1 ( ( 1 0.7 3 ) × ( 1 0.3 3 ) ) 1 2 ) × ( 1 ( ( 1 0.7 3 ) × ( 1 0.2 3 ) ) 1 2 ) × ( 1 ( ( 1 0.3 3 ) × ( 1 0.2 3 ) ) 1 2 ) ) 3 ) 1 C 4 2 ] ) = ( [   0.4261 , 0.7072 ] , [   0.3604 , 0.4605 ] )
The q-RIVOFHM satisfies the following three properties.
Property 1.
Idempotency: if q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) are equal, then
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = q ˜
Proof. 
Since q ˜ j = q ˜ = ( [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] ) , then
q -RIVOFHM ( x ) ( q ˜ , q ˜ , , q ˜ ) = 1 i 1 < < i x n ( j = 1 x q ˜ ) 1 x C n x = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ L ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ R ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ L ) q ) ) 1 x q ) 1 C n x , ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( v q ˜ R ) q ) ) 1 x q ) 1 C n x ] } = { [ 1 ( ( 1 ( ( u q ˜ L ) x ) q x ) C n x ) 1 C n x q , 1 ( ( 1 ( ( u q ˜ R ) x ) q x ) C n x ) 1 C n x q ] , [ ( ( 1 ( ( 1 ( v q ˜ L ) q ) x ) 1 x q ) C n x ) 1 C n x , ( ( 1 ( ( 1 ( v q ˜ R ) q ) x ) 1 x q ) C n x ) 1 C n x ] } = { [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] } = q ˜
 □
Property 2.
Monotonicity: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) and q ˜ j = ( [ ( u q ˜ j L ) , ( u q ˜ j R ) ] , [ ( v q ˜ j L ) , ( v q ˜ j R ) ] ) ( j = 1 , 2 , , n ) be two sets of q-RIVOFNs. If u q ˜ j L ( u q ˜ j L ) , u q ˜ j R ( u q ˜ j R ) , v q ˜ j L ( v q ˜ j L )   and   v q ˜ j L ( v q ˜ j R ) hold for all j , then
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n )
Proof. 
Given that u q ˜ j L ( u q ˜ j L ) , we can obtain
( j = 1 x u q ˜ L ) q x ( j = 1 x ( u q ˜ L ) ) q x
( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ L ) q x ) ) 1 C n x ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ L ) ) q x ) ) 1 C n x
Thereafter,
1 ( 1 i 1 < < i x n ( 1 ( j = 1 x u q ˜ L ) q x ) ) 1 C n x q 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ L ) ) q x ) ) 1 C n x q
That means u q ˜ L ( u q ˜ L ) . Similarly, we can obtain u q ˜ R ( u q ˜ R ) , v q ˜ L ( v q ˜ L )   and   v q ˜ L ( v q ˜ R ) . Thus, the proof is complete. □
Property 3.
Boundedness: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. If q ˜ + = ( [ max i ( u q ˜ j L ) , max i ( u q ˜ j R ) ] , [ min i ( v q ˜ j L ) , min i ( v q ˜ j R ) ] ) and q ˜ = ( [ min i ( u q ˜ j L ) , min i ( u q ˜ j R ) ] , [ max i ( v q ˜ j L ) , max i ( v q ˜ j R ) ] ) then
q ˜ q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +
From Property 1,
q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = q ˜ q -RIVOFHM ( x ) ( q ˜ 1 + , q ˜ 2 + , , q ˜ n + ) = q ˜ +
From Property 2,
q ˜ q -RIVOFHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +

3.2. The q-RIVOFWHM Operator

In practical MADM problems, it is important to take the attribute weights into account. This section will develop the q-rung interval-valued orthopair, fuzzy weighted Hamy mean (q-RIVOFWHM) operator.
Definition 6.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs, with their weight vector as w i = ( w 1 , w 2 , , w n ) T , thereby satisfying w i [ 0 , 1 ] and i = 1 n w i = 1 . Then we can define the q-RIVOFWHM operator as follows:
q -RIVOFWHM w ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x ( q ˜ i j ) w i j ) 1 x C n x
Theorem 2.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. The fused value obtained by using q-RIVOFWHM operator is also a q-RIVOFN, where
q -RIVOFWHM w ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x ( q ˜ i j ) w i j ) 1 x C n x = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j L ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j R ) w i j ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x q ) ) 1 C n x ] }
Proof. 
From Definition 3, we can obtain
( q ˜ i j ) w i j = { [ ( u q ˜ j L ) w i j , ( u q ˜ j R ) w i j ] , [ 1 ( 1 ( v q ˜ j L ) q ) w i j q , 1 ( 1 ( v q ˜ j R ) q ) w i j q ] }
Thus,
j = 1 x ( q ˜ i j ) w i j = { [ j = 1 x ( u q ˜ j L ) w i j , j = 1 x ( u q ˜ j R ) w i j ] , [ 1 j = 1 x ( 1 ( v q ˜ j L ) q ) w i j q , 1 j = 1 x ( 1 ( v q ˜ j R ) q ) w i j q ] }
Therefore,
( j = 1 x ( q ˜ i j ) w i j ) 1 x = { [ ( j = 1 x ( u q ˜ j L ) w i j ) 1 x , ( j = 1 x ( u q ˜ j R ) w i j ) 1 x ] , [ 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) w i j ) 1 x q , 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x q ] }
Thereafter,
1 i 1 < < i x n ( j = 1 x ( q ˜ i j ) w i j ) 1 x = { [ 1 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j L ) w i j ) q x ) q , 1 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j R ) w i j ) q x ) q ] , [ 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) w i j ) 1 x q ) , 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x q ) ] }
Furthermore,
q -RIVOFWHM w ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x ( q ˜ i j ) w i j ) 1 x C n x = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j L ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j R ) w i j ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j L ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x q ) ) 1 C n x ] }
Hence, Equation (16) is kept.
Then we need to prove that Equation (16) is a q-RIVOFN. We need to prove that 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 .
Let
μ Q ˜ R ( x ) = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j R ) w i j ) q x ) ) 1 C n x q ν Q ˜ R ( x ) = ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x q ) ) 1 C n x
 □
Proof. 
0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( u q ˜ j R ) w i j ) q x ) ) 1 C n x + ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x ) ) 1 C n x 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x ) ) 1 C n x + ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( v q ˜ j R ) q ) w i j ) 1 x ) ) 1 C n x = 1
Therefore, 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 is maintained. □
Example 2.
Let ( [ 0.5 , 0.8 ] , [ 0.4 , 0.5 ] ) , ( [ 0.3 , 0.5 ] , [ 0.6 , 0.7 ] ) , ( [ 0.5 , 0.7 ] , [ 0.2 , 0.3 ] ) and   ( [ 0.4 , 0.8 ] , [ 0.1 , 0.2 ] ) be four q-RIVOFNs, and w = ( 0.2 , 0.1 , 0.3 , 0.4 ) ; in addition, suppose x   =   2 ,   q   =   3 . Then, according to Equation (16), we have
q -RIVOFWHM w ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = 1 i 1 < < i x n ( j = 1 x ( q ˜ i j ) w i j ) 1 x C n x = ( [ 1 ( ( 1 ( 0.5 0.2 × 0.3 0.1 ) 3 2 ) × ( 1 ( 0.5 0.2 × 0.5 0.3 ) 3 2 ) × ( 1 ( 0.5 0.2 × 0.4 0.4 ) 3 2 ) × ( 1 ( 0.3 0.1 × 0.5 0.3 ) 3 2 ) × ( 1 ( 0.3 0.1 × 0.4 0.4 ) 3 2 ) × ( 1 ( 0.5 0.3 × 0.4 0.4 ) 3 2 ) ) 1 C 4 2 3 , 1 ( ( 1 ( 0.8 0.2 × 0.5 0.1 ) 3 2 ) × ( 1 ( 0.8 0.2 × 0.7 0.3 ) 3 2 ) × ( 1 ( 0.8 0.2 × 0.8 0.4 ) 3 2 ) × ( 1 ( 0.5 0.1 × 0.7 0.3 ) 3 2 ) × ( 1 ( 0.5 0.1 × 0.8 0.4 ) 3 2 ) × ( 1 ( 0.7 0.3 × 0.8 0.4 ) 3 2 ) ) 1 C 4 2 3 ] , [ ( ( ( 1 ( ( 1 0.4 3 ) 0.2 × ( 1 0.6 3 ) 0.1 ) 1 2 ) × ( 1 ( ( 1 0.4 3 ) 0.2 × ( 1 0.2 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.4 3 ) 0.2 × ( 1 0.1 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.6 3 ) 0.1 × ( 1 0.2 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.6 3 ) 0.1 × ( 1 0.1 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.2 3 ) 0.3 × ( 1 0.1 3 ) 0.4 ) 1 2 ) ) 3 ) 1 C 4 2 , ( ( ( 1 ( ( 1 0.5 3 ) 0.2 × ( 1 0.7 3 ) 0.1 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.2 × ( 1 0.3 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.2 × ( 1 0.2 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.7 3 ) 0.1 × ( 1 0.3 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 07 3 ) 0.1 × ( 1 0.2 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.3 3 ) 0.3 × ( 1 0.2 3 ) 0.4 ) 1 2 ) ) 3 ) 1 C 4 2 ] ) = ( [   0.8204 , 0.9266 ] , [   0.1983 , 0.2589 ] )
The q-RIVOFWHM operator satisfies the following properties.
Property 4.
Monotonicity: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) and q ˜ j = ( [ ( u q ˜ j L ) , ( u q ˜ j R ) ] , [ ( v q ˜ j L ) , ( v q ˜ j R ) ] ) ( j = 1 , 2 , , n ) be two sets of q-RIVOFNs. If u q ˜ j L ( u q ˜ j L ) , u q ˜ j R ( u q ˜ j R ) , v q ˜ j L ( v q ˜ j L )   and   v q ˜ j L ( v q ˜ j R ) hold for all j , then
q -RIVOFWHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q -RIVOFWHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n )
The proof is similar to q-RIVOFHM, so it is omitted here.
Property 5.
Boundedness: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. If q ˜ + = ( [ max i ( u q ˜ j L ) , max i ( u q ˜ j R ) ] , [ min i ( v q ˜ j L ) , min i ( v q ˜ j R ) ] ) and q ˜ = ( [ min i ( u q ˜ j L ) , min i ( u q ˜ j R ) ] , [ max i ( v q ˜ j L ) , max i ( v q ˜ j R ) ] ) then
q ˜ q -RIVOFWHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +
From Theorem 2, we get
q -RIVOFWHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( min ( u q ˜ j L ) ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( min ( u q ˜ j R ) ) w i j ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( max ( v q ˜ j L ) ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( max ( v q ˜ j R ) ) q ) w i j ) 1 x q ) ) 1 C n x ] }
q -RIVOFWHM ( x ) ( q ˜ 1 + , q ˜ 2 + , , q ˜ n + ) = { [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( max ( u q ˜ j L ) ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( max ( u q ˜ j R ) ) w i j ) q x ) ) 1 C n x q ] , [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( min ( v q ˜ j L ) ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( min ( v q ˜ j R ) ) q ) w i j ) 1 x q ) ) 1 C n x ] }
From Property 4, we get
q ˜ q -RIVOFWHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +
It is obvious that the q-RIVOFWHM operator lacks the property of idempotency.

3.3. The q-RIVOFDHM Operator

Wu et al. [61] define the dual Hamy mean (DHM) operator.
Definition 7
[61].The DHM operator can be defined as:
DHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x q ˜ i j x ) ) 1 C n x
where x is a parameter, and x = 1 , 2 , , n , i 1 , i 2 , , i x are x integer values taken from the set { 1 , 2 , , n } of k integer values; C n x denotes the binomial coefficient and C n x = n ! x ! ( n x ) ! .
In this section, we will propose the q-rung interval-valued orthopair, fuzzy DHM (q-RIVOFDHM) operator.
Definition 8.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. The q-RIVOFDHM operator is
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x q ˜ i j x ) ) 1 C n x
Theorem 3.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. The fused value by using q-RIVOFDHM operators is also a q-RIVOFN, where
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x q ˜ i j x ) ) 1 C n x = { [ ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x q ) 1 C n x , ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x q ) 1 C n x ] , [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j L ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j R ) q x ) ) 1 C n x q ] }
Proof. 
j = 1 x q ˜ i j = { [ 1 j = 1 x ( 1 ( u q ˜ j L ) q ) q , 1 j = 1 x ( 1 ( u q ˜ j R ) q ) q ] , [ j = 1 x v q ˜ j L , j = 1 x v q ˜ j R ] }
Thus,
j = 1 x q ˜ i j x = { [ 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x q , 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x q ] , [ ( j = 1 x v q ˜ j L ) 1 x , ( j = 1 x v q ˜ j R ) 1 x ] }
Thereafter,
1 i 1 < < i x n ( j = 1 x q ˜ i j x ) = { [ 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x q , 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x q ] [ 1 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j L ) q x ) q , 1 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j R ) q x ) q ] }
Therefore,
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x q ˜ i j x ) ) 1 C n x = { [ ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x q ) 1 C n x , ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x q ) 1 C n x ] , [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j L ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j R ) q x ) ) 1 C n x q ] }
Hence, Equation (29) is kept.
Then, we need to prove that Equation (29) is a q-RIVOFN. We need to prove that 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 .
Let
μ Q ˜ R ( x ) = ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x q ) 1 C n x ν Q ˜ R ( x ) = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j R ) q x ) ) 1 C n x q
 □
Proof. 
0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j R ) q x ) ) 1 C n x + ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x ) ) 1 C n x 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x ) ) 1 C n x + ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x ) ) 1 C n x = 1
Therefore, 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 is maintained. □
Example 3.
Let ( [ 0.5 , 0.8 ] , [ 0.4 , 0.5 ] ) , ( [ 0.3 , 0.5 ] , [ 0.6 , 0.7 ] ) , ( [ 0.5 , 0.7 ] , [ 0.2 , 0.3 ] ) and   ( [ 0.4 , 0.8 ] , [ 0.1 , 0.2 ] ) be four q-RIVOFNs, and suppose x   =   2 ,   q   =   3 ; then according to Equation (29), we have
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x q ˜ i j x ) ) 1 C n x = ( [ ( ( ( 1 ( ( 1 0.5 3 ) × ( 1 0.3 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.5 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.4 3 ) ) 1 2 ) × ( 1 ( ( 1 0.3 3 ) × ( 1 0.5 3 ) ) 1 2 ) × ( 1 ( ( 1 0.3 3 ) × ( 1 0.4 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.4 3 ) ) 1 2 ) ) 3 ) 1 C 4 2 , ( ( ( 1 ( ( 1 0.8 3 ) × ( 1 0.5 3 ) ) 1 2 ) × ( 1 ( ( 1 0.8 3 ) × ( 1 0.7 3 ) ) 1 2 ) × ( 1 ( ( 1 0.8 3 ) × ( 1 0.8 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.7 3 ) ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) × ( 1 0.8 3 ) ) 1 2 ) × ( 1 ( ( 1 0.7 3 ) × ( 1 0.8 3 ) ) 1 2 ) ) 3 ) 1 C 4 2 ] , [ 1 ( ( 1 ( 0.4 × 0.6 ) 3 2 ) × ( 1 ( 0.4 × 0.2 ) 3 2 ) × ( 1 ( 0.4 × 0.1 ) 3 2 ) × ( 1 ( 0.6 × 0.2 ) 3 2 ) × ( 1 ( 0.6 × 0.1 ) 3 2 ) × ( 1 ( 0.2 × 0.1 ) 3 2 ) ) 1 C 4 2 3 , 1 ( ( 1 ( 0.5 × 0.7 ) 3 2 ) × ( 1 ( 0.5 × 0.3 ) 3 2 ) × ( 1 ( 0.5 × 0.2 ) 3 2 ) × ( 1 ( 0.7 × 0.3 ) 3 2 ) × ( 1 ( 0.7 × 0.2 ) 3 2 ) × ( 1 ( 0.3 × 0.2 ) 3 2 ) ) 1 C 4 2 3 ] ) = ( [ 0.4348 , 0.7214 ] , [ 0.3283 , 0.4291 ] )
The q-RIVOFDHM has the following three operators.
Property 6.
Idempotency: if q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) are equal, then
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = q ˜
Proof. 
Since q ˜ j = q ˜ = ( [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] ) , then
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x q ˜ i j x ) ) 1 C n x = { [ ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x q ) 1 C n x , ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) ) 1 x q ) 1 C n x ] , [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j L ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x v q ˜ j R ) q x ) ) 1 C n x q ] } = { [ ( ( 1 ( ( 1 ( u q ˜ L ) q ) x ) 1 x q ) C n x ) 1 C n x , ( ( 1 ( ( 1 ( u q ˜ R ) q ) x ) 1 x q ) C n x ) 1 C n x ] , [ 1 ( ( 1 ( ( v q ˜ L ) x ) q x ) C n x ) 1 C n x q , 1 ( ( 1 ( ( v q ˜ R ) x ) q x ) C n x ) 1 C n x q ] , } = { [ u q ˜ L , u q ˜ R ] , [ v q ˜ L , v q ˜ R ] } = q ˜
 □
Property 7.
Monotonicity: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) and q ˜ j = ( [ ( u q ˜ j L ) , ( u q ˜ j R ) ] , [ ( v q ˜ j L ) , ( v q ˜ j R ) ] ) ( j = 1 , 2 , , n ) be two sets of q-RIVOFNs. If u q ˜ j L ( u q ˜ j L ) , u q ˜ j R ( u q ˜ j R ) , v q ˜ j L ( v q ˜ j L )   and   v q ˜ j L ( v q ˜ j R ) hold for all j , then
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n )
Proof. 
Given that u q ˜ j L ( u q ˜ j L ) , we can obtain
j = 1 x ( 1 ( u q ˜ j L ) q ) j = 1 x ( 1 ( ( u q ˜ j L ) ) q )
1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x 1 ( j = 1 x ( 1 ( ( u q ˜ j L ) ) q ) ) 1 x
Thereafter,
( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) ) 1 x q ) 1 C n x ( 1 i 1 < < i x n 1 ( j = 1 x ( 1 ( ( u q ˜ j L ) ) q ) ) 1 x q ) 1 C n x
That means that u q ˜ L ( u q ˜ L ) . Similarly, we can obtain u q ˜ R ( u q ˜ R ) , v q ˜ L ( v q ˜ L )   and   v q ˜ L ( v q ˜ R ) . Thus, the proof is complete. □
Property 8.
Boundedness: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. If q ˜ + = ( [ max i ( u q ˜ j L ) , max i ( u q ˜ j R ) ] , [ min i ( v q ˜ j L ) , min i ( v q ˜ j R ) ] ) and q ˜ = ( [ min i ( u q ˜ j L ) , min i ( u q ˜ j R ) ] , [ max i ( v q ˜ j L ) , max i ( v q ˜ j R ) ] ) then
q ˜ q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +
From Property 6,
q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = q ˜ q -RIVOFDHM ( x ) ( q ˜ 1 + , q ˜ 2 + , , q ˜ n + ) = q ˜ +
From Property 7,
q ˜ q -RIVOFDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +

3.4. The q-RIVOFWDHM Operator

In real MADM problems, it’s of necessity to take attribute weights into account; we will propose the q-rung interval-valued orthopair fuzzy weighted DHM (q-RIVOFWDHM) operator in this chapter.
Definition 9.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs, with their weight vector as w i = ( w 1 , w 2 , , w n ) T , thereby satisfying w i [ 0 , 1 ] and i = 1 n w i = 1 . If
q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x w i j q ˜ i j x ) ) 1 C n x
Theorem 4.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. The fused value by using q-RIVOFWDHM operators is also a q-RIVOFN, where
q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x w i j q ˜ i j x ) ) 1 C n x = { [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x q ) ) 1 C n x ] [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j L ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j R ) w i j ) q x ) ) 1 C n x q ] }
Proof. 
w i j q ˜ i j = { [ 1 ( 1 ( u q ˜ j L ) q ) w i j q , 1 ( 1 ( u q ˜ j R ) q ) w i j q ] , [ ( v q ˜ j L ) w i j , ( v q ˜ j R ) w i j ] }
Thus,
j = 1 x ( w i j q ˜ i j ) = { [ 1 j = 1 x ( 1 ( u q ˜ j L ) q ) w i j q , 1 j = 1 x ( 1 ( u q ˜ j R ) q ) w i j q ] , [ j = 1 x ( v q ˜ j L ) w i j , j = 1 x ( v q ˜ j R ) w i j ] }
Therefore,
j = 1 x ( w i j q ˜ i j ) x = { [ 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) w i j ) 1 x q , 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x q ] [ ( j = 1 x ( v q ˜ j L ) w i j ) 1 x , ( j = 1 x ( v q ˜ j R ) w i j ) 1 x ] }
Thereafter,
1 i 1 < < i x n ( j = 1 x w i j q ˜ i j x ) = { [ 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) w i j ) 1 x q ) , 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x q ) ] [ 1 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j L ) w i j ) q x ) q , 1 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j R ) w i j ) q x ) q ] }
Furthermore,
q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x w i j q ˜ i j x ) ) 1 C n x = { [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j L ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x q ) ) 1 C n x ] [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j L ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j R ) w i j ) q x ) ) 1 C n x q ] }
Hence, Equation (41) is kept.
Then, we need to prove that Equation (41) is a q-RIVOFN. We need to prove that 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 .
Let
μ Q ˜ R ( x ) = ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x q ) ) 1 C n x ν Q ˜ R ( x ) = 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j R ) w i j ) q x ) ) 1 C n x q
 □
Proof. 
0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q = ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x ) ) 1 C n x + 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( v q ˜ j R ) w i j ) q x ) ) 1 C n x ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x ) ) 1 C n x + 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( u q ˜ j R ) q ) w i j ) 1 x ) ) 1 C n x = 1
Therefore, 0 ( μ Q ˜ R ( x ) ) q + ( ν Q ˜ R ( x ) ) q 1 is maintained. □
Example 4.
Let ( [ 0.5 , 0.8 ] , [ 0.4 , 0.5 ] ) , ( [ 0.3 , 0.5 ] , [ 0.6 , 0.7 ] ) , ( [ 0.5 , 0.7 ] , [ 0.2 , 0.3 ] ) and   ( [ 0.4 , 0.8 ] , [ 0.1 , 0.2 ] ) be four q-RIVOFNs; suppose x   =   2 ,   q   =   3 , and ω   =   ( 0.2 , 0.1 , 0.3 , 0.4 ) . Then, based on Equation (41), we can get
q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = ( 1 i 1 < < i x n ( j = 1 x w i j q ˜ i j x ) ) 1 C n x = ( [ ( ( ( 1 ( ( 1 0.5 3 ) 0.2 × ( 1 0.3 3 ) 0.1 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.2 × ( 1 0.5 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.2 × ( 1 0.4 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.3 3 ) 0.1 × ( 1 0.5 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.3 3 ) 0.1 × ( 1 0.4 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.3 × ( 1 0.4 3 ) 0.4 ) 1 2 ) ) 3 ) 1 C 4 2 , ( ( ( 1 ( ( 1 0.8 3 ) 0.2 × ( 1 0.5 3 ) 0.1 ) 1 2 ) × ( 1 ( ( 1 0.8 3 ) 0.2 × ( 1 0.7 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.8 3 ) 0.2 × ( 1 0.8 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.1 × ( 1 0.7 3 ) 0.3 ) 1 2 ) × ( 1 ( ( 1 0.5 3 ) 0.1 × ( 1 0.8 3 ) 0.4 ) 1 2 ) × ( 1 ( ( 1 0.7 3 ) 0.3 × ( 1 0.8 3 ) 0.4 ) 1 2 ) ) 3 ) 1 C 4 2 ] , [ 1 ( ( 1 ( 0.4 0.2 × 0.6 0.1 ) 3 2 ) × ( 1 ( 0.4 0.2 × 0.2 0.3 ) 3 2 ) × ( 1 ( 0.4 0.2 × 0.1 0.4 ) 3 2 ) × ( 1 ( 0.6 0.1 × 0.2 0.3 ) 3 2 ) × ( 1 ( 0.6 0.1 × 0.1 0.4 ) 3 2 ) × ( 1 ( 0.2 0.3 × 0.1 0.4 ) 3 2 ) ) 1 C 4 2 3 , 1 ( ( 1 ( 0.5 0.2 × 0.7 0.1 ) 3 2 ) × ( 1 ( 0.5 0.2 × 0.3 0.3 ) 3 2 ) × ( 1 ( 0.5 0.2 × 0.2 0.4 ) 3 2 ) × ( 1 ( 0.7 0.1 × 0.3 0.3 ) 3 2 ) × ( 1 ( 0.7 0.1 × 0.2 0.4 ) 3 2 ) × ( 1 ( 0.3 0.3 × 0.2 0.4 ) 3 2 ) ) 1 C 4 2 3 ] ) = ( [ 0.2819 , 0.4954 ] , [ 0.7249 , 0.7855 ] )
We will then study some precious properties of q-RIVOFWDHM operator.
Property 9.
Monotonicity: let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) and q ˜ j = ( [ ( u q ˜ j L ) , ( u q ˜ j R ) ] , [ ( v q ˜ j L ) , ( v q ˜ j R ) ] ) ( j = 1 , 2 , , n ) be two sets of q-RIVOFNs. If u q ˜ j L ( u q ˜ j L ) , u q ˜ j R ( u q ˜ j R ) , v q ˜ j L ( v q ˜ j L )   and   v q ˜ j L ( v q ˜ j R ) hold for all j , then
q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n )
This proof is similar to q-RIVOFDHM, so it is omitted here.
Property 10.
(Boundedness) Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs. If q ˜ + = ( [ max i ( u q ˜ j L ) , max i ( u q ˜ j R ) ] , [ min i ( v q ˜ j L ) , min i ( v q ˜ j R ) ] ) and q ˜ = ( [ min i ( u q ˜ j L ) , min i ( u q ˜ j R ) ] , [ max i ( v q ˜ j L ) , max i ( v q ˜ j R ) ] ) then
q ˜ q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +
From Theorem 4, we get
q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = { [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( min ( u q ˜ j L ) ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( min ( u q ˜ j R ) ) q ) w i j ) 1 x q ) ) 1 C n x ] [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( max ( v q ˜ j L ) ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( max ( v q ˜ j R ) ) w i j ) q x ) ) 1 C n x q ] }
q -RIVOFWDHM ( x ) ( q ˜ 1 + , q ˜ 2 + , , q ˜ n + ) = { [ ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( max ( u q ˜ j L ) ) q ) w i j ) 1 x q ) ) 1 C n x , ( 1 i 1 < < i x n ( 1 ( j = 1 x ( 1 ( max ( u q ˜ j R ) ) q ) w i j ) 1 x q ) ) 1 C n x ] [ 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( min ( v q ˜ j L ) ) w i j ) q x ) ) 1 C n x q , 1 ( 1 i 1 < < i x n ( 1 ( j = 1 x ( min ( v q ˜ j R ) ) w i j ) q x ) ) 1 C n x q ] }
From Property 9, we get
q ˜ q -RIVOFWDHM ( x ) ( q ˜ 1 , q ˜ 2 , , q ˜ n ) q ˜ +
It is obvious that the q-RIVOFWDHM operator is short of the property of idempotency.

4. Application of Green Supplier Selection

4.1. Numerical Example

With the rapid development of economic globalization, and the growing enterprise competition environment, the competition between modern enterprises has become the competition between supply chains. The diversity of the people consuming is increasing, and the new product life cycles are getting shorter. The volatility of the demand market and from external factors drives enterprises for effective supply chain integration and management, as well as strategic alliances with other enterprises to enhance core competitiveness and resist external risk. The key measure to achieving this goal is supplier selection. Therefore, the supplier selection problem has gained a lot of attention, whether in regard to supply chain management theory or in actual production management problems [62,63,64,65,66,67,68,69,70]. In order to illustrate our proposed method in this article, we provide a numerical example for selecting green suppliers in green supply chain management using q-RIVOFNs. There is a panel with five possible green suppliers in green supply chain management to select: Q ˜ i ( i = 1 , 2 , 3 , 4 , 5 ) . The experts select four attributes to evaluate the five possible green suppliers: (1) C1 is the product quality factor; (2) C2 is the environmental factors; (3) C3 is the delivery factor; and (4) C4 is the price factor. The five possible green suppliers Q ˜ i ( i = 1 , 2 , 3 , 4 , 5 ) are to be evaluated by the decision maker using the q-RIVOFNs, under the above four attributes (whose weighting vector ω = ( 0.3 , 0.2 , 0.3 , 0.2 ) , and expert weighting vector ω = ( 0.2 , 0.2 , 0.6 ) ) which are listed in Table 1, Table 2 and Table 3.
In the following, we utilize the approach developed to select green suppliers in green supply chain management.
Step 1. According to q-RIVOFNs q ˜ i j ( i = 1 , 2 , 3 , 4 , 5 , j = 1 , 2 , 3 , 4 ) , we can aggregate all q-RIVOFNs q ˜ i j by using the q-RIVOFWA (q-RIVOFWG) operator, to get the overall q-RIVOFNs Q ˜ i ( i = 1 , 2 , 3 , 4 , 5 ) of the green suppliers Q ˜ i . Then, the fused values are given in Table 4. (Let q = 3 ).
Definition 10.
Let q ˜ j = ( [ u q ˜ j L , u q ˜ j R ] , [ v q ˜ j L , v q ˜ j R ] ) ( j = 1 , 2 , , n ) be a set of q-RIVOFNs, with their weight vector as w i = ( w 1 , w 2 , , w n ) T , thereby satisfying w i [ 0 , 1 ] and i = 1 n w i = 1 . Then we can obtain
q -RIVOFWA ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = j = 1 n w j q ˜ j = [ 1 j = 1 n ( 1 u q ˜ j L ) w j q , 1 j = 1 n ( 1 u q ˜ j R ) w j q ] , [ j = 1 n ( v q ˜ j L ) w j , j = 1 n ( v q ˜ j R ) w j ]
q -RIVOFWG ( q ˜ 1 , q ˜ 2 , , q ˜ n ) = j = 1 n ( q ˜ j ) w j = [ j = 1 n ( u q ˜ j L ) w j , j = 1 n ( u q ˜ j R ) w j ] , [ 1 j = 1 n ( 1 v q ˜ j L ) w j q , 1 j = 1 n ( 1 v q ˜ j R ) w j q ]
Step 2. Based on Table 4, we can fuse all q-RIVOFNs q ˜ i j by the q-RIVOFWHM (q-RIVOFWDHM) operator to get the results of q-RIVOFNs. Let x = 2 , then the fused values are given in Table 5.
Step 3. Based on the fused values given in Table 5, and the score functions of q-RIVOFNs, the green suppliers’ scores are shown in Table 6.
Step 4. Rank all the alternatives by the values of Table 6, and the ordering results are shown in Table 7. Obviously, the best selection is Q ˜ 3 .

4.2. Influence of the Parameter x

In order to show the effects on the ranking results, by changing parameters of x in the q-RIVOFWHM (q-RIVOFWDHM) operators, all of the results are shown in Table 8 and Table 9. (Let q = 3 ).

4.3. Influence of the Parameter q

In order to show the effects on the ranking results by changing the parameters of q in the q-RIVOFWHM (q-RIVOFWDHM) operators, all of the results are shown in Table 10 and Table 11. (Let x = 2 ).

4.4. Comparative Analysis

In this chapter, we compare the q-RIVOFWHM and q-RIVOFWDHM operators with the q-RIVOFWA and q-RIVOFWG operators. The comparative results are shown in Table 12.
From above, we can see that we get the same optimal green suppliers, which shows the practicality and effectiveness of the proposed approaches. However, the q-RIVOFWA operator and q-RIVOFWG operator do not consider the information about the relationship between arguments being aggregated, and thus cannot eliminate the influence of unfair arguments on decision results. Our proposed q-RIVOFWHM and q-RIVOFWDHM operators consider the information about the relationship among arguments being aggregated.
At the same time, Liu and Wang [54] develop the q-rung orthopair, fuzzy weighted averaging (q-ROFWA) operator, as well as the q-rung orthopair, fuzzy weighted geometric (q-ROFWG) operator. Liu and Liu [55] propose some q-rung orthopair, fuzzy Bonferroni mean (q-ROFBM) aggregation operators. Wei et al. [56] define the generalized Heronian mean (GHM) operator to present some aggregation operators, and apply them into MADM problems. Wei et al. [57] define some q-rung orthopair, fuzzy Maclaurin symmetric mean operators. However, all of these operators can only deal with q-rung orthopair fuzzy sets (q-ROFSs), and cannot deal with q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs). The main contribution of this paper is to study the MAGDM problems based on the q-rung interval-valued orthopair fuzzy sets (q-RIVOFSs), and to utilize the Hamy mean (HM) operator, weighted Hamy mean (WHM) operator, dual Hamy mean (DHM) operator, and weighted dual Hamy mean (WDHM) operator, to develop some Hamy mean aggregation operators with q-RIVOFNs.

5. Conclusions

In this paper, we study the MAGDM problems with q-RIVOFNs. Then, we utilize the Hamy mean (HM) operator, weighted Hamy mean (WHM) operator, dual Hamy mean (DHM) operator, and weighted dual Hamy mean (WDHM) operator, in order to develop some Hamy mean aggregation operators with q-RIVOFNs. The prominent characteristic of each of these proposed operators is studied. Then, we have utilized these operators to develop some approaches to solve the MAGDM problems with q-RIVOFNs. Finally, a practical example for green supplier selection is given to show the developed approach. Using the illustrated example, we have roughly shown the effects on the ranking results by changing parameters in the q-RIVOFWHM (q-RIVOFWDHM) operators. In the future, the application of the proposed fused operators of q-RIVOFNs needs to be explored in decision making [71,72,73,74], risk analysis [75,76], and many other fields under uncertain environments [77,78,79,80,81].

Author Contributions

J.W., H.G., G.W. and Y.W. conceived and worked together to achieve this work, J.W. compiled the computing program by Excel and analyzed the data, J.W. and G.W. wrote the paper. Finally, all the authors have read and approved the final manuscript.

Funding

The work was supported by the National Natural Science Foundation of China under Grant No. 71571128 and the Humanities and Social Sciences Foundation of Ministry of Education of the People’s Republic of China (17XJA630003) and the Construction Plan of Scientific Research Innovation Team for Colleges and Universities in Sichuan Province (15TD0004).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Zadeh, L.A. Fuzzy sets. Inf. Control 1965, 8, 338–356. [Google Scholar] [CrossRef]
  2. Atanassov, K. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986, 20, 87–96. [Google Scholar] [CrossRef]
  3. Atanassov, K. Two theorems for intuitionistic fuzzy sets. Fuzzy Sets Syst. 2000, 110, 267–269. [Google Scholar] [CrossRef]
  4. Wu, L.; Wei, G.; Gao, H.; Wei, Y. Some interval-valued intuitionistic fuzzy dombi hamy mean operators and their application for evaluating the elderly tourism service quality in tourism destination. Mathematics 2018, 6, 294. [Google Scholar] [CrossRef]
  5. Wang, R.; Wang, J.; Gao, H.; Wei, G. Methods for MADM with picture fuzzy muirhead mean operators and their application for evaluating the financial investment risk. Symmetry 2019, 11, 6. [Google Scholar] [CrossRef]
  6. Li, Z.; Gao, H.; Wei, G. Methods for multiple attribute group decision making based on intuitionistic fuzzy dombi hamy mean operators. Symmetry 2018, 10, 574. [Google Scholar] [CrossRef]
  7. Deng, X.M.; Wei, G.W.; Gao, H.; Wang, J. Models for safety assessment of construction project with some 2-tuple linguistic Pythagorean fuzzy Bonferroni mean operators. IEEE Access 2018, 6, 52105–52137. [Google Scholar] [CrossRef]
  8. Gao, H. Pythagorean fuzzy hamacher prioritized aggregation operators in multiple attribute decision making. J. Intell. Fuzzy Syst. 2018, 35, 2229–2245. [Google Scholar] [CrossRef]
  9. Wei, G.; Wei, Y. Some single-valued neutrosophic dombi prioritized weighted aggregation operators in multiple attribute decision making. J. Intell. Fuzzy Syst. 2018, 35, 2001–2013. [Google Scholar] [CrossRef]
  10. Wang, J.; Wei, G.; Gao, H. Approaches to multiple attribute decision making with interval-valued 2-tuple linguistic pythagorean fuzzy information. Mathematics 2018, 6, 201. [Google Scholar] [CrossRef]
  11. Wei, G.W. TODIM method for picture fuzzy multiple attribute decision making. Informatica 2018, 29, 555–566. [Google Scholar] [CrossRef]
  12. Xu, Z.S. intuitionistic fuzzy aggregation operators. IEEE Trans. Fuzzy Syst. 2007, 15, 1179–1187. [Google Scholar]
  13. Wei, G.W.; Garg, H.; Gao, H.; Wei, C. Interval-Valued pythagorean fuzzy maclaurin symmetric mean operators in multiple attribute decision making. IEEE Access 2018, 6, 67866–67884. [Google Scholar] [CrossRef]
  14. Wei, G.W.; Wei, C.; Gao, H. multiple attribute decision making with interval-valued bipolar fuzzy information and their application to emerging technology commercialization evaluation. IEEE Access 2018, 6, 60930–60955. [Google Scholar] [CrossRef]
  15. Wang, J.; Wei, G.; Lu, M. An Extended VIKOR Method for Multiple Criteria Group Decision Making with Triangular Fuzzy Neutrosophic Numbers. Symmetry 2018, 10, 497. [Google Scholar] [CrossRef]
  16. Wang, J.; Wei, G.; Lu, M. TODIM Method for multiple attribute group decision making under 2-Tuple Linguistic Neutrosophic Environment. Symmetry 2018, 10, 486. [Google Scholar] [CrossRef]
  17. Li, Z.; Wei, G.; Lu, M. Pythagorean fuzzy hamy mean operators in multiple attribute group decision making and their application to supplier selection. Symmetry 2018, 10, 505. [Google Scholar] [CrossRef]
  18. Wei, G.W. Some geometric aggregation functions and their application to dynamic multiple attribute decision making in intuitionistic fuzzy setting. Int. J. Uncertain. Fuzz. Knowl.-Based Syst. 2009, 17, 179–196. [Google Scholar] [CrossRef]
  19. Li, Z.; Wei, G.; Gao, H. Methods for multiple attribute decision making with interval-valued pythagorean fuzzy information. Mathematics 2018, 6, 228. [Google Scholar] [CrossRef]
  20. Deng, X.; Wang, J.; Wei, G.; Lu, M. Models for multiple attribute decision making with some 2-tuple linguistic pythagorean fuzzy hamy mean operators. Mathematics 2018, 6, 236. [Google Scholar] [CrossRef]
  21. Tang, X.Y.; Wei, G.W. Models for green supplier selection in green supply chain management with Pythagorean 2-tuple linguistic information. IEEE Access 2018, 6, 18042–18060. [Google Scholar] [CrossRef]
  22. Ye, J. Fuzzy decision-making method based on the weighted correlation coefficient under intuitionistic fuzzy environment. Eur. J. Oper. Res. 2010, 205, 202–204. [Google Scholar] [CrossRef]
  23. Huang, Y.H.; Wei, G.W. TODIM Method for Pythagorean 2-tuple linguistic multiple attribute decision making. J. Intell. Fuzzy Syst. 2018, 35, 901–915. [Google Scholar] [CrossRef]
  24. Wei, G.W.; Gao, H.; Wang, J.; Huang, Y.H. Research on risk evaluation of enterprise human capital investment with Interval-valued bipolar 2-tuple linguistic Information. IEEE Access 2018, 6, 35697–35712. [Google Scholar] [CrossRef]
  25. Liang, X.; Wei, C. An Atanassov’s intuitionistic fuzzy multi-attribute group decision making method based on entropy and similarity measure. Int. J. Mach. Learn. Cybern. 2014, 5, 435–444. [Google Scholar] [CrossRef]
  26. Yager, R.R. Pythagorean fuzzy subsets. In Proceedings of the Joint IFSA World Congress and NAFIPS Annual Meeting, Emonton, AB, Canada, 21 June 2013; pp. 57–61. [Google Scholar]
  27. Yager, R.R. Pythagorean membership grades in multicriteria decision making. IEEE Trans. Fuzzy Syst. 2014, 22, 958–965. [Google Scholar] [CrossRef]
  28. Zhang, X.L.; Xu, Z.S. Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. Int. J. Intell. Syst. 2014, 29, 1061–1078. [Google Scholar] [CrossRef]
  29. Wei, G.W.; Lu, M. Pythagorean Fuzzy Maclaurin Symmetric Mean Operators in multiple attribute decision making. Int. J. Intell. Syst. 2018, 33, 1043–1070. [Google Scholar] [CrossRef]
  30. Peng, X.; Yang, Y. Some results for Pythagorean Fuzzy Sets. Int. J. Intell. Syst. 2015, 30, 1133–1160. [Google Scholar] [CrossRef]
  31. Wei, G.W.; Lu, M. Pythagorean fuzzy power aggregation operators in multiple attribute decision making. Int. J. Intell. Syst. 2018, 33, 169–186. [Google Scholar] [CrossRef]
  32. Wei, G.W. Interval valued hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making. Int. J. Mach. Learn. Cybern. 2016, 7, 1093–1114. [Google Scholar] [CrossRef]
  33. Wei, G.W. Picture fuzzy Hamacher aggregation operators and their application to multiple attribute decision making. Fundam. Inf. 2018, 157, 271–320. [Google Scholar] [CrossRef]
  34. Gao, H.; Lu, M.; Wei, G.W.; Wei, Y. Some Novel Pythagorean Fuzzy Interaction Aggregation Operators in Multiple Attribute Decision Making. Fundam. Inf. 2018, 159, 385–428. [Google Scholar] [CrossRef]
  35. Wei, G.W. Interval valued hesitant fuzzy uncertain linguistic aggregation operators in multiple attribute decision making. Int. J. Mach. Learn. Cybern. 2016, 7, 1093–1114. [Google Scholar] [CrossRef]
  36. Wei, G.W.; Zhao, X.F.; Wang, H.J.; Lin, R. Fuzzy power aggregating operators and their application to multiple attribute group decision making. Technol. Econ. Dev. Econ. 2013, 19, 377–396. [Google Scholar] [CrossRef]
  37. Wei, G.W. Some linguistic power aggregating operators and their application to multiple attribute group decision making. J. Intell. Fuzzy Syst. 2013, 25, 695–707. [Google Scholar]
  38. Beliakov, G.; James, S. Averaging aggregation functions for preferences expressed as Pythagorean membership grades and fuzzy orthopairs. In Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 6–11 July 2014; pp. 298–305. [Google Scholar]
  39. Reformat, M.; Yager, R.R. Suggesting Recommendations Using Pythagorean Fuzzy Sets illustrated Using Netflix Movie Data. In Proceedings of the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Montpellier, France, 15–19 July 2014; pp. 546–556. [Google Scholar]
  40. Gou, X.; Xu, Z.; Ren, P. The Properties of Continuous Pythagorean Fuzzy Information. Int. J. Intell. Syst. 2016, 31, 401–424. [Google Scholar] [CrossRef]
  41. Wei, G.W.; Wei, Y. Similarity measures of Pythagorean fuzzy sets based on cosine function and their applications. Int. J. Intell. Syst. 2018, 33, 634–652. [Google Scholar] [CrossRef]
  42. Wei, G.W.; Gao, H. The Generalized Dice Similarity Measures for Picture Fuzzy Sets and Their Applications. Informatica 2018, 29, 107–124. [Google Scholar] [CrossRef]
  43. Wei, G.W. Some similarity measures for picture fuzzy sets and their applications. Iran. J. Fuzzy Syst. 2018, 15, 77–89. [Google Scholar]
  44. Wei, G.W. Some Cosine Similarity Measures for Picture Fuzzy Sets and Their Applications to Strategic Decision Making. Informatica 2017, 28, 547–564. [Google Scholar] [CrossRef]
  45. Wei, G.W.; Lin, R.; Wang, H.J. Distance and similarity measures for hesitant interval-valued fuzzy sets. J. Intell. Fuzzy Syst. 2014, 27, 19–36. [Google Scholar]
  46. Ren, P.; Xu, Z.; Gou, X. Pythagorean fuzzy TODIM approach to multi-criteria decision making. Appl. Soft Comput. 2016, 42, 246–259. [Google Scholar] [CrossRef]
  47. Garg, H. A New Generalized Pythagorean Fuzzy Information Aggregation Using Einstein Operations and Its Application to Decision Making. Int. J. Intell. Syst. 2016, 31, 886–920. [Google Scholar] [CrossRef]
  48. Zeng, S.; Chen, J.; Li, X. A Hybrid Method for Pythagorean Fuzzy Multiple-Criteria Decision Making. Int. J. Inf. Technol. Decis. Mak. 2016, 15, 403–422. [Google Scholar] [CrossRef]
  49. Garg, H. A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem. J. Intell. Fuzzy Syst. 2016, 31, 529–540. [Google Scholar] [CrossRef]
  50. Wei, G.W.; Lu, M.; Tang, X.Y.; Wei, Y. Pythagorean Hesitant Fuzzy Hamacher Aggregation Operators and Their Application to Multiple Attribute Decision Making. Int. J. Intell. Syst. 2018, 33, 1197–1233. [Google Scholar] [CrossRef]
  51. Wei, G.W.; Lu, M. Dual hesitant pythagorean fuzzy Hamacher aggregation operators in multiple attribute decision making. Arch. Control Sci. 2017, 27, 365–395. [Google Scholar] [CrossRef]
  52. Lu, M.; Wei, G.W.; Alsaadi, F.E.; Hayat, T.; Alsaedi, A. Hesitant Pythagorean fuzzy Hamacher aggregation operators and their application to multiple attribute decision making. J. Intell. Fuzzy Syst. 2017, 33, 1105–1117. [Google Scholar] [CrossRef]
  53. Yager, R.R. Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 2017, 25, 1222–1230. [Google Scholar] [CrossRef]
  54. Liu, P.D.; Wang, P. Some q-Rung Orthopair Fuzzy Aggregation Operators and their Applications to Multiple-Attribute Decision Making. Int. J. Intell. Syst. 2018, 32, 259–280. [Google Scholar] [CrossRef]
  55. Liu, P.D.; Liu, J.L. Some q-Rung Orthopai Fuzzy Bonferroni Mean Operators and Their Application to Multi-Attribute Group Decision Making. Int. J. Intell. Syst. 2018, 33, 315–347. [Google Scholar] [CrossRef]
  56. Wei, G.W.; Gao, H.; Wei, Y. Some q-Rung Orthopair Fuzzy Heronian Mean Operators in Multiple Attribute Decision Making. Int. J. Intell. Syst. 2018, 33, 1426–1458. [Google Scholar] [CrossRef]
  57. Wei, G.W.; Wei, C.; Wang, J.; Gao, H.; Wei, Y. Some q-rung orthopair fuzzy maclaurin symmetric mean operators and their applications to potential evaluation of emerging technology commercialization. Int. J. Intell. Syst. 2019, 34, 50–81. [Google Scholar] [CrossRef]
  58. Atanassov, K.; Gargov, G. Interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 1989, 31, 343–349. [Google Scholar] [CrossRef]
  59. Atanassov, K. Operators over interval-valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 1994, 64, 159–174. [Google Scholar] [CrossRef]
  60. Hara, T.; Uchiyama, M.; Takahasi, S.E. A refinement of various mean inequalities. J. Inequal. Appl. 1998, 2, 387–395. [Google Scholar] [CrossRef]
  61. Wu, S.; Wang, J.; Wei, G.; Wei, Y. Research on Construction Engineering Project Risk Assessment with Some 2-Tuple Linguistic Neutrosophic Hamy Mean Operators. Sustainability 2018, 10, 1536. [Google Scholar] [CrossRef]
  62. Wang, J.; Wei, G.W.; Wei, Y. Models for Green Supplier Selection with Some 2-Tuple Linguistic Neutrosophic Number Bonferroni Mean Operators. Symmetry 2018, 10, 131. [Google Scholar] [CrossRef]
  63. Gao, H.; Wei, G.W.; Huang, Y.H. Dual hesitant bipolar fuzzy Hamacher prioritized aggregation operators in multiple attribute decision making. IEEE Access 2018, 6, 11508–11522. [Google Scholar] [CrossRef]
  64. Wei, G.W.; Alsaadi, F.E.; Hayat, T.; Alsaedi, A. Projection models for multiple attribute decision making with picture fuzzy information. Int. J. Mach. Learn. Cybern. 2018, 9, 713–719. [Google Scholar] [CrossRef]
  65. Wang, S.Y. Applying 2-tuple multigranularity linguistic variables to determine the supply performance in dynamic environment based on product-oriented strategy. IEEE Trans. Fuzzy Syst. 2008, 16, 29–39. [Google Scholar] [CrossRef]
  66. Wei, G.W.; Alsaadi, F.E.; Hayat, T.; Alsaedi, A. Bipolar fuzzy Hamacher aggregation operators in multiple attribute decision making. Int. J. Fuzzy Syst. 2018, 20, 1–12. [Google Scholar] [CrossRef]
  67. You, X.Y.; You, J.X.; Liu, H.C.; Zhen, L. Group multi-criteria supplier selection using an extended VIKOR method with interval 2-tuple linguistic information. Expert Syst. Appl. 2015, 42, 1906–1916. [Google Scholar] [CrossRef]
  68. Wei, G.W.; Alsaadi, F.E.; Hayat, T.; Alsaedi, A. Picture 2-tuple linguistic aggregation operators in multiple attribute decision making. Soft Comput. 2018, 22, 989–1002. [Google Scholar] [CrossRef]
  69. Santos, L.; Osiro, L.; Lima, R.H.P. A model based on 2-tuple fuzzy linguistic representation and Analytic Hierarchy Process for supplier segmentation using qualitative and quantitative criteria. Expert Syst. Appl. 2017, 79, 53–64. [Google Scholar] [CrossRef]
  70. Wei, G.W.; Wang, J.M. A comparative study of robust efficiency analysis and Data Envelopment Analysis with imprecise data. Expert Syst. Appl. 2017, 81, 28–38. [Google Scholar] [CrossRef]
  71. Wei, G.W. Picture uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making. Kbernetes 2017, 46, 1777–1800. [Google Scholar] [CrossRef]
  72. Wei, G.W. Picture 2-tuple linguistic Bonferroni mean operators and their application to multiple attribute decision making. Int. J. Fuzzy Syst. 2017, 19, 997–1010. [Google Scholar] [CrossRef]
  73. Wei, G.W.; Alsaadi, F.E.; Hayat, T.; Alsaedi, A. Hesitant bipolar fuzzy aggregation operators in multiple attribute decision making. J. Intell. Fuzzy Syst. 2017, 33, 1119–1128. [Google Scholar] [CrossRef]
  74. Li, L.; Zhang, R.; Wang, J.; Shang, X.; Bai, K. A Novel Approach to Multi-Attribute Group Decision-Making with q-Rung Picture Linguistic Information. Symmetry 2018, 10, 172. [Google Scholar] [CrossRef]
  75. Wei, Y.; Liu, J.; Lai, X.; Hu, Y. Which determinant is the most informative in forecasting crude oil market volatility: Fundamental, speculation, or uncertainty? Energy Econ. 2017, 68, 141–150. [Google Scholar] [CrossRef]
  76. Wei, Y.; Yu, Q.; Liu, J.; Cao, Y. Hot money and China’s stock market volatility: Further evidence using the GARCH-MIDAS model. Physica A 2018, 492, 923–930. [Google Scholar] [CrossRef]
  77. Alcantud, J.C.R.; de Andrés Calle, R.; Cascón, J.M. A unifying model to measure consensus solutions in a society. Math. Comput. Model. 2013, 57, 1876–1883. [Google Scholar] [CrossRef]
  78. Alcantud, J.C.R.; de Andrés Calle, R.; Cascón, J.M. On measures of cohesiveness under dichotomous opinions: Some characterizations of approval consensus measures. Inf. Sci. 2013, 240, 45–55. [Google Scholar] [CrossRef]
  79. Ullah, K.; Mahmood, T.; Jan, N. Similarity Measures for T-Spherical Fuzzy Sets with Applications in Pattern Recognition. Symmetry 2018, 10, 193. [Google Scholar] [CrossRef]
  80. Zhu, H.; Zhao, J.B.; Xu, Y. 2-dimension linguistic computational model with 2-tuples for multi-attribute group decision making. Knowl.-Based Syst. 2016, 103, 132–142. [Google Scholar] [CrossRef]
  81. Wei, C.P.; Liao, H.C. A Multigranularity Linguistic Group Decision-Making Method Based on Hesitant 2-Tuple Sets. Int. J. Intell. Syst. 2016, 31, 612–634. [Google Scholar] [CrossRef]
Table 1. The q-RIVOFN decision matrix 1 (R1) by expert one.
Table 1. The q-RIVOFN decision matrix 1 (R1) by expert one.
Alternatives C1C2C3C4
Q ˜ 1 ([0.4,0.5],[0.5,0.7])([0.6,0.7],[0.2,0.3])([0.3,0.5],[0.4,0.6])([0.7,0.8],[0.2,0.4])
Q ˜ 2 ([0.2,0.3],[0.4,0.5])([0.1,0.2],[0.6,0.7])([0.6,0.8],[0.2,0.3])([0.5,0.6],[0.5,0.7])
Q ˜ 3 ([0.7,0.9],[0.1,0.2])([0.4,0.5],[0.2,0.3])([0.5,0.7],[0.3,0.4])([0.6,0.7],[0.1,0.2])
Q ˜ 4 ([0.3,0.5],[0.4,0.6])([0.2,0.3],[0.1,0.2])([0.5,0.6],[0.1,0.5])([0.3,0.4],[0.2,0.3])
Q ˜ 5 ([0.3,0.6],[0.2,0.4])([0.4,0.6],[0.2,0.3])([0.1,0.2],[0.4,0.5])([0.2,0.4],[0.1,0.3])
Table 2. The q-RIVOFN decision matrix 1 (R2) by expert two.
Table 2. The q-RIVOFN decision matrix 1 (R2) by expert two.
AlternativesC1C2C3C4
Q ˜ 1 ([0.3,0.4],[0.4,0.6])([0.7,0.8],[0.3,0.4])([0.2,0.4],[0.3,0.5])([0.8,0.9],[0.3,0.5])
Q ˜ 2 ([0.1,0.2],[0.3,0.4])([0.2,0.3],[0.7,0.8])([0.5,0.7],[0.1,0.2])([0.6,0.7],[0.6,0.8])
Q ˜ 3 ([0.6,0.8],[0.1,0.2])([0.5,0.6],[0.3,0.4])([0.4,0.6],[0.2,0.3])([0.7,0.8],[0.2,0.3])
Q ˜ 4 ([0.2,0.4],[0.3,0.5])([0.3,0.4],[0.2,0.3])([0.4,0.5],[0.1,0.4])([0.4,0.5],[0.3,0.4])
Q ˜ 5 ([0.2,0.5],[0.1,0.3])([0.5,0.7],[0.3,0.4])([0.1,0.2],[0.3,0.4])([0.3,0.5],[0.2,0.4])
Table 3. The q-RIVOFN decision matrix 1 (R3) by expert three.
Table 3. The q-RIVOFN decision matrix 1 (R3) by expert three.
AlternativesC1C2C3C4
Q ˜ 1 ([0.5,0.6],[0.6,0.8])([0.5,0.6],[0.1,0.2])([0.4,0.6],[0.5,0.7])([0.6,0.7],[0.1,0.3])
Q ˜ 2 ([0.3,0.4],[0.5,0.6])([0.1,0.2],[0.5,0.6])([0.7,0.9],[0.3,0.4])([0.4,0.5],[0.4,0.6])
Q ˜ 3 ([0.8,0.9],[0.2,0.3])([0.3,0.4],[0.1,0.2])([0.6,0.8],[0.4,0.5])([0.5,0.6],[0.1,0.2])
Q ˜ 4 ([0.4,0.6],[0.5,0.7])([0.1,0.2],[0.1,0.2])([0.6,0.7],[0.2,0.6])([0.2,0.3],[0.1,0.2])
Q ˜ 5 ([0.4,0.7],[0.3,0.5])([0.3,0.5],[0.1,0.2])([0.2,0.3],[0.5,0.6])([0.1,0.3],[0.1,0.2])
Table 4. The fused results from the q-RIVOFWA operator.
Table 4. The fused results from the q-RIVOFWA operator.
AlternativesC1C2
Q ˜ 1 ([0.7637,0.8175],[0.5335,0.7354])([0.8283,0.8756],[0.1431,0.2491])
Q ˜ 2 ([0.6249,0.7011],[0.4317,0.5335])([0.4945,0.6047],[0.5547,0.6554])
Q ˜ 3 ([0.9089,0.9601],[0.1516,0.2551])([0.7149,0.7756],[0.1431,0.2491])
Q ˜ 4 ([0.7011,0.8175],[0.4317,0.6346])([0.5474,0.6420],[0.1149,0.2169])
Q ˜ 5 ([0.7011,0.8654],[0.2221,0.4317])([0.7149,0.8283],[0.1431,0.2491])
AlternativesC3C4
Q ˜ 1 ([0.7011,0.8175],[0.4317,0.6346])([0.8756,0.9197],[0.1431,0.3519])
Q ˜ 2 ([0.8654,0.9498],[0.2221,0.3288])([0.7756,0.8283],[0.4536,0.6554])
Q ˜ 3 ([0.8175,0.9089],[0.3288,0.4317])([0.8283,0.8756],[0.1149,0.2169])
Q ˜ 4 ([0.8175,0.8654],[0.1516,0.5335])([0.6420,0.7149],[0.1431,0.2491])
Q ˜ 5 ([0.5445,0.6396],[0.4317,0.5335])([0.5474,0.7149],[0.1149,0.2491])
Table 5. The fused values of the q-rung interval-valued orthopair, fuzzy weighted Hamy mean (q-RIVOFWHM) and the q-rung interval-valued orthopair, fuzzy weighted dual Hamy mean (q-RIVOFWDHM)) operators.
Table 5. The fused values of the q-rung interval-valued orthopair, fuzzy weighted Hamy mean (q-RIVOFWHM) and the q-rung interval-valued orthopair, fuzzy weighted dual Hamy mean (q-RIVOFWDHM)) operators.
Alternativesq-RIVOFWHMq-RIVOFWDHM
Q ˜ 1 ([0.9422,0.9616],[0.2248,0.3558])([0.5409,0.6039],[0.7423,0.8415])
Q ˜ 2 ([0.9148,0.9418],[0.2710,0.3562])([0.4842,0.5611],[0.7959,0.8530])
Q ˜ 3 ([0.9536,0.9720],[0.1237,0.1901])([0.5790,0.6546],[0.6536,0.7346])
Q ˜ 4 ([0.9112,0.9379],[0.1415,0.2910])([0.4637,0.5318],[0.6697,0.8006])
Q ˜ 5 ([0.8903,0.9356],[0.1575,0.2505])([0.4140,0.5250],[0.6861,0.7805])
Table 6. The score values s ( Q ˜ i ) of the green suppliers.
Table 6. The score values s ( Q ˜ i ) of the green suppliers.
Alternativesq-RIVOFWHMq-RIVOFWDHM
Q ˜ 1 0.91720.3434
Q ˜ 2 0.88400.2914
Q ˜ 3 0.94420.4497
Q ˜ 4 0.88850.3591
Q ˜ 5 0.87620.3543
Table 7. Ordering of the green suppliers.
Table 7. Ordering of the green suppliers.
MethodsOrdering
q-RIVOFWHM Q ˜ 3 > Q ˜ 1 > Q ˜ 4 > Q ˜ 2 > Q ˜ 5
q-RIVOFWDHM Q ˜ 3 > Q ˜ 4 > Q ˜ 5 > Q ˜ 1 > Q ˜ 2
Table 8. Ordering results for different x values by the q-RIVOFWHM operator.
Table 8. Ordering results for different x values by the q-RIVOFWHM operator.
Parameters S ( Q ˜ 1 )   S ( Q ˜ 2 ) S ( Q ˜ 3 ) S ( Q ˜ 4 ) S ( Q ˜ 5 ) Ordering
x = 1 0.93060.89930.94760.89410.8844 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
x = 2 0.91720.88400.94420.88850.8762 Q ˜ 3 > Q ˜ 1 > Q ˜ 4 > Q ˜ 2 > Q ˜ 5
x = 3 0.92900.89590.94540.89470.8786 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
x = 4 0.90800.87720.94190.88390.8703 Q ˜ 3 > Q ˜ 1 > Q ˜ 4 > Q ˜ 2 > Q ˜ 5
Table 9. Ordering results for different x values by the q-RIVOFWDHM operator.
Table 9. Ordering results for different x values by the q-RIVOFWDHM operator.
Parameters S ( Q ˜ 1 )   S ( Q ˜ 2 ) S ( Q ˜ 3 ) S ( Q ˜ 4 ) S ( Q ˜ 5 ) Ordering
x = 1 0.33300.25790.43400.34240.3464 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
x = 2 0.34340.29140.44970.35910.3543 Q ˜ 3 > Q ˜ 4 > Q ˜ 5 > Q ˜ 1 > Q ˜ 2
x = 3 0.25570.22920.34060.30050.3024 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
x = 4 0.34860.31500.45850.36790.3586 Q ˜ 3 > Q ˜ 4 > Q ˜ 5 > Q ˜ 1 > Q ˜ 2
Table 10. Ordering results for different q by the q-RIVOFWHM operator.
Table 10. Ordering results for different q by the q-RIVOFWHM operator.
Parameters S ( Q ˜ 1 ) S ( Q ˜ 2 ) S ( Q ˜ 3 ) S ( Q ˜ 4 ) S ( Q ˜ 5 ) Ordering
q = 1 0.90900.88990.94810.91470.9121 Q ˜ 3 > Q ˜ 4 > Q ˜ 5 > Q ˜ 1 > Q ˜ 2
q = 2 0.92440.89820.95550.91090.9031 Q ˜ 3 > Q ˜ 1 > Q ˜ 4 > Q ˜ 5 > Q ˜ 2
q = 3 0.91720.88400.94420.88850.8762 Q ˜ 3 > Q ˜ 1 > Q ˜ 4 > Q ˜ 2 > Q ˜ 5
q = 4 0.90330.86340.92930.86270.8469 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
q = 5 0.88720.84120.91390.83710.8187 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
q = 6 0.87050.81930.89890.81270.7926 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
q = 7 0.85400.79830.88440.78990.7687 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
q = 8 0.83800.77850.87040.76870.7468 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
q = 9 0.82250.76000.85700.74900.7270 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
q = 10 0.80780.74270.84410.73080.7089 Q ˜ 3 > Q ˜ 1 > Q ˜ 2 > Q ˜ 4 > Q ˜ 5
Table 11. Ordering results for different q by the q-RIVOFWDHM operator.
Table 11. Ordering results for different q by the q-RIVOFWDHM operator.
Parameters S ( Q ˜ 1 )   S ( Q ˜ 2 ) S ( Q ˜ 3 ) S ( Q ˜ 4 ) S ( Q ˜ 5 ) Ordering
q = 1 0.28140.24150.35200.27560.2655 Q ˜ 3 > Q ˜ 1 > Q ˜ 4 > Q ˜ 5 > Q ˜ 2
q = 2 0.31070.26170.40740.31880.3110 Q ˜ 3 > Q ˜ 4 > Q ˜ 5 > Q ˜ 1 > Q ˜ 2
q = 3 0.34340.29140.44970.35910.3543 Q ˜ 3 > Q ˜ 4 > Q ˜ 5 > Q ˜ 1 > Q ˜ 2
q = 4 0.37220.32040.47880.39130.3893 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
q = 5 0.39620.34640.49780.41610.4163 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
q = 6 0.41570.36890.50980.43500.4369 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
q = 7 0.43140.38810.51710.44940.4524 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
q = 8 0.44410.40440.52110.46040.4641 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
q = 9 0.45430.41810.52300.46880.4729 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
q = 10 0.46250.42970.52350.47540.4795 Q ˜ 3 > Q ˜ 5 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2
Table 12. Comparative results.
Table 12. Comparative results.
MethodsOrdering
q-RIVOFWA Q ˜ 3 > Q ˜ 4 > Q ˜ 1 > Q ˜ 2 > Q ˜ 5
q-RIVOFWG Q ˜ 3 > Q ˜ 2 > Q ˜ 5 > Q ˜ 1 > Q ˜ 4

Share and Cite

MDPI and ACS Style

Wang, J.; Gao, H.; Wei, G.; Wei, Y. Methods for Multiple-Attribute Group Decision Making with q-Rung Interval-Valued Orthopair Fuzzy Information and Their Applications to the Selection of Green Suppliers. Symmetry 2019, 11, 56. https://doi.org/10.3390/sym11010056

AMA Style

Wang J, Gao H, Wei G, Wei Y. Methods for Multiple-Attribute Group Decision Making with q-Rung Interval-Valued Orthopair Fuzzy Information and Their Applications to the Selection of Green Suppliers. Symmetry. 2019; 11(1):56. https://doi.org/10.3390/sym11010056

Chicago/Turabian Style

Wang, Jie, Hui Gao, Guiwu Wei, and Yu Wei. 2019. "Methods for Multiple-Attribute Group Decision Making with q-Rung Interval-Valued Orthopair Fuzzy Information and Their Applications to the Selection of Green Suppliers" Symmetry 11, no. 1: 56. https://doi.org/10.3390/sym11010056

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop