GE2-VIKOR MCDM METHOD APPROACH FOR DEFENSE TECHNOLOGY CRITICALITY ASSESSMENT IN DEVELOPING COUNTRIES

Torres, Pablo Santos; Cunha, Antonio Eduardo Carrilho da; Santos, Marcos dos

doi:10.1590/0101-7438.2023.043.00287021

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ARTICLES • Pesqui. Oper. 44 • 2024 • https://doi.org/10.1590/0101-7438.2023.043.00287021 linkcopy

GE2-VIKOR MCDM METHOD APPROACH FOR DEFENSE TECHNOLOGY CRITICALITY ASSESSMENT IN DEVELOPING COUNTRIES

Authorship SCIMAGO INSTITUTIONS RANKINGS

This paper introduces the Group Extended Entropy VIKOR (GE2-VIKOR) MCDM approach for assessing defense technology criticality in developing countries. GE2-VIKOR addresses uncertainty and complexity in this scenario by leveraging VIKOR’s ability to propose compromise solutions in discrete alternatives’ scenarios. GE2-VIKOR can be applied in both individual and group decision-making, incorporating weights based on decision-makers’ knowledge degree and criteria weights determined by the sponsor or Shannon’s entropy theory. The method aims to offer a compromise solution that maximizes group utility while minimizing individual regret under majority, consensus, or veto rules. A detailed demonstration illustrates GE2-VIKOR’s applicability and effectiveness in assessing defense technology criticality in developing countries. The results highlight the method’s strengths and limitations and suggest future research avenues, particularly exploring its applicability in developed countries, where technology criticality concepts differ.

Keywords:
multicriteria decision making; GE2-VIKOR; technology criticality

TRL cluster	Designation
1	Initial research (TRL 1-3)
2	Technological development (TRL 4-6)
3	Integration and prototype design (TRL 7-9)
4	Production and user experimentation (TRL 10-11)

Negligible importance	Minor importance	Moderate importance	Significant importance	Crucial importance
1	2	3	4	5

Objective	$f_{j}^{*}$	$f_{j}^{-}$
Benefit	$f_{j}^{*} = \underset{i}{m a x} f_{i j}$	$f_{j}^{-} = \underset{i}{m i n} f_{i j}$
Cost	$f_{j}^{*} = \underset{i}{m i n} f_{i j}$	$f_{j}^{-} = \underset{i}{m a x} f_{i j}$

Best	Worst
$S * = \underset{i}{m i n} S_{i}$	$S^{-} = \underset{i}{m a x} S_{i}$
$R * = \underset{i}{m i n} R_{i}$	$R^{-} = \underset{i}{m a x} R_{i}$

Decision-maker 1				Decision-maker 2				Decision-maker 3
Criteria	C1	C2	C3	Criteria	C1	C2	C3	Criteria	C1	C2	C3
Objective	B	C	B	Objective	B	C	B	Objective	B	C	B
T1	2	3	2	T1	1	4	1	T1	2	2	4
T2	1	2	1	T2	1	1	2	T2	1	1	1
T3	3	3	3	T3	3	1	3	T3	3	1	4
T4	2	2	4	T4	3	2	2	T4	2	3	3
T5	3	2	2	T5	1	1	2	T5	3	2	1

Criteria	C1	C2	C3
Objective	B	C	B
T1	1.61538	3	2.38462
T2	1	1.23077	1.38462
T3	3	1.46154	3.38462
T4	2.38462	2.38462	2.84615
T5	2.23077	1.61538	1.61538

Criteria	C1	C2	C3
${\bar{H}}_{S}^{(j)}$	0.96308	0.96553	0.96733
$D_{S}^{n}$	0.03692	0.03447	0.03267
w	0.35476	0.33129	0.31295

	ν = 0.5			ν = 0.25			ν = 0.75
	Si	Ri	Qi	Si	Ri	Qi	Si	Ri	Qi
T1	0.73387	0.33129	0.96232	0.73387	0.33129	0.94348	0.73387	0.33129	0.98116
T2	0.66871	0.35476	0.95283	0.66871	0.35476	0.97642	0.66871	0.35476	0.92925
T3	0.04321	0.04321	0.00000	0.04321	0.04321	0.00000	0.04321	0.04321	0.00000
T4	0.40974	0.21606	0.54274	0.40974	0.21606	0.54876	0.40974	0.21606	0.53672
T5	0.48619	0.27772	0.69705	0.48619	0.27772	0.72488	0.48619	0.27772	0.66922

	ν = 0.5		ν = 0.25		ν = 0.75
	Q _i	Rank	Q _i	Rank	Q _i	Rank
T1	0.96232	5	0.94348	4	0.98116	5
T2	0.95283	4	0.97642	5	0.92925	4
T3	0	1	0	1	0	1
T4	0.54274	2	0.54876	2	0.53672	2
T5	0.69705	3	0.72488	3	0.66922	3

Quartile	ν = 0.5	ν = 0.25	ν = 0.75
Q1	T3	T3	T3
Q2	T4, T5	T4, T5	T4, T5
Q3	T2	T1	T2
Q4	T1	T2	T1

Criteria	ν = 0.5	ν = 0.25	ν = 0.75
C1	0 ≤ w ₁ ≤ 0.35476	0.34767 ≤ w ₁ ≤ 1	0.04257 ≤ w ₁ ≤ 0.38315
C2	0.33129 ≤ w ₂ ≤ 0.39755	0.27166 ≤ w ₂ ≤ 0.33791	0.31472 ≤ w ₂ ≤ 0.41080
C3	0.25430 ≤ w ₃ ≤ 0.32964	0.26058 ≤ w ₃ ≤ 0.37360	0.24174 ≤ w ₃ ≤ 0.35476

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[TFN1] Source: Girardi et al. (2024) under CC BY-NC-ND 4.0 license.