基于累积前景理论和学识评级的PL-VIKOR群决策

doi:10.12305/j.issn.1001-506X.2023.06.20

Abstract

Abstract:

In order to solve the problems of unreasonable evaluation information extraction in the probabilistic language environment, unscientific and inefficient decision-making caused by differences in knowledge, and insufficient discrimination of traditional sorting methods, an improved probabilistic linguistic vlsekriterijumska optimizacija I kompromisno resenje (PL-VIKOR) based on cumulative prospect theory and knowledge rating group decision method is proposed is this paper. Firstly, the term value and probability value of probabilistic language are fused into a comprehensive value based on cumulative prospect theory. Secondly, the comprehensive weight is determined by combining subjective self-assessment and objective social rating to maximize the professional contribution. Then, a modified formula adjusted by the comprehensive weight is established to achieve the group consensus quickly. Further, the performance of the VIKOR method is improved by introducing the cosine value. Finally, the effectiveness and rationality of the proposed method are verified through the site selection application and comparative analysis of pumped energy storage projects.

Key words: multi-criteria group decision-making (MGDM), cumulative prospect theory, knowledge rating, consensus-reaching, improved probabilistic linguistic vlsekriterijumska optimizacija I kompromisno resenje (VIKOR)

CLC Number:

TP311.52

Yu ZHANG, Lei WANG, Lin LU, Yongping YE, Liang WAN. PL-VIKOR group decision-making based on cumulative prospect theory and knowledge rating[J]. Systems Engineering and Electronics, 2023, 45(6): 1762-1771.

Figures/Tables 11

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References 37

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专家	方案	c₁	c₂	c₃	c₄	c₅
e₁	a₁	s_-2(0.1), s_-1(0.9)	s₀ (0.4), s₁ (0.6)	s₁ (0.3), s₂ (0.7)	s₀ (0.4), s₁ (0.6)	s_-2 (0.8), s_-1 (0.2)
	a₂	s_-1 (0.6), s₀ (0.4)	s₀ (1)	s₀ (0.7), s₁ (0.3)	s₁ (1)	s₀ (0.5), s₁ (0.5)
	a₃	s_-1 (0.6), s₀ (0.4)	s₀ (0.3), s₁ (0.7)	s₀ (0.7), s₁ (0.3)	s₀ (1)	s₀ (0.5), s₁ (0.5)
	a₄	s₀ (0.2), s₁ (0.8)	s₀ (0.1), s₁ (0.9)	s_-2 (0.7), s_-1 (0.1), s₀ (0.2)	s₀ (0.1), s₁ (0.9)	s_-2 (0.1), s_-1 (0.1), s₀ (0.8)
e₂	a₁	s_-1 (0.1), s₀ (0.9)	-	s_-2 (0.1), s_-1 (0.9)	-	s_-2 (0.1), s_-1 (0.9)
	a₂	s_-1 (0.2), s₀ (0.8)	-	s_-1 (0.2), s₀ (0.8)	-	s_-1 (0.2), s₀ (0.8)
	a₃	s_-1 (0.2), s₀ (0.8)	-	s_-1 (0.2), s₀ (0.8)	-	s_-1 (0.2), s₀ (0.8)
	a₄	s_-1 (0.1), s₀ (0.9)	-	s₀ (0.9), s₁ (0.1)	-	s_-1 (0.1), s₀ (0.9)

e₁₆	a₁	s_-1 (0.1), s₀ (0.9)	s₁ (0.5), s₂ (0.5)	s_-1 (0.1), s₀ (0.9)	s₁ (0.5), s₂ (0.5)	-
	a₂	s_-1 (0.2), s₀ (0.8)	s_-1 (0.2), s₀ (0.3), s₁ (0.5)	s_-1 (0.2), s₀ (0.8)	s_-1 (0.2), s₀ (0.5), s₁ (0.3)	-
	a₃	s_-1 (0.2), s₀ (0.8)	s_-1 (0.2), s₀ (0.5), s₁ (0.3)	s_-1 (0.2), s₀ (0.8)	s_-1 (0.2), s₀ (0.5), s₁ (0.3)	-
	a₄	s₀ (0.1), s₁ (0.9)	s_-2 (0.1), s_-1 (0.8), s₀ (0.1)	s₀ (0.1), s₁ (0.9)	s_-2 (0.1), s_-1 (0.8), s₀ (0.1)	-

专家	方案	c₁	c₂	c₃	c₄	c₅
e₁	a₁	-2.448 1	0.473 9	1.300 8	0.473 9	-3.348 4
	a₂	-1.165 7	0.000 0	0.318 4	1.000 0	0.420 6
	a₃	-1.165 7	0.533 8	0.318 4	0.000 0	0.420 6
	a₄	0.607 4	0.711 7	-2.816 7	0.711 7	-1.087 4
e₂	a₁	-0.382 8	0.000 0	-2.448 1	0.000 0	-2.448 1
	a₂	-0.578 3	0.000 0	-0.578 3	0.000 0	-0.578 3
	a₃	-0.578 3	0.000 0	-0.578 3	0.000 0	-0.578 3
	a₄	-0.382 8	0.000 0	0.186 3	0.000 0	-0.382 8

e₁₆	a₁	-0.382 8	1.194 8	-0.382 8	1.194 8	0.000 0
	a₂	-0.578 3	-0.157 7	-0.578 3	-0.259 9	0.000 0
	a₃	-0.578 3	-0.259 9	-0.578 3	-0.259 9	0.000 0
	a₄	0.711 7	-2.209 7	0.711 7	-2.209 7	0.000 0

专家	权值	c₁	c₂	c₃	c₄	c₅
e₁	γ	0.900 0	0.900 0	0.900 0	0.900 0	0.900 0
	μ	0.900 0	0.300 0	0.100 0	0.300 0	0.700 0
	η	0.810 0	0.270 0	0.090 0	0.270 0	0.630 0
	λ	0.187 1	0.060 8	0.014 9	0.055 4	0.130 7
e₂	γ	0.900 0	0.900 0	0.900 0	0.900 0	0.900 0
	μ	0.100 0	0.000 0	0.900 0	0.000 0	0.900 0
	η	0.090 0	0.000 0	0.810 0	0.000 0	0.810 0
	λ	0.020 8	0.000 0	0.133 7	0.000 0	0.168 0

e₁₆	γ	0.200 0	0.200 0	0.200 0	0.200 0	0.200 0
	μ	0.600 0	0.100 0	0.800 0	0.100 0	0
	η	0.120 0	0.020 0	0.160 0	0.020 0	0
	λ	0.027 7	0.004 5	0.026 4	0.004 1	0

方案	c₁	c₂	c₃	c₄	c₅
a₁	-1.156 9	-0.534 6	-1.457 5	-0.534 6	-2.000 2
a₂	-0.829 6	-0.192 9	-0.565 8	-0.146 2	0.201 2
a₃	-0.829 6	-0.151 3	-0.565 8	-0.204 3	0.201 2
a₄	-0.135 6	0.021 9	0.278 6	0.242 3	-1.446 4

方案	S	R	cos	Q	排序
a₁	1.000 0	1.000 0	1.000 0	1.000 0	4
a₂	0.339 5	0.360 4	0.000 0	0.279 9	2
a₃	0.339 5	0.360 4	0.052 1	0.290 4	3
a₄	0.000 0	0.000 0	0.203 9	0.040 8	1

PL-VIKOR group decision-making based on cumulative prospect theory and knowledge rating

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Figures/Tables 11

References 37

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方法	e¹	e²	e³	e⁴	e⁵	e⁶	e⁷	e⁸	e⁹	e¹⁰	e¹¹	e¹²	e¹³	e¹⁴	e¹⁵	e¹⁶
文献[13]	0.082	0.079	0.073	0.072	0.063	0.065	0.060	0.061	0.066	0.058	0.058	0.058	0.056	0.054	0.050	0.046
文献[14]	0.025	0.085	0.025	0.085	0.025	0.085	0.085	0.025	0.025	0.085	0.025	0.085	0.085	0.085	0.085	0.085
本文方法	0.175	0.019	0.069	0.017	0.136	0.030	0.117	0.065	0.117	0.000	0.086	0.022	0.054	0.009	0.058	0.026

方法	修正程序	修正公式	优点	缺点
文献[6]	直接修正	V^(t+1)= $\frac{1}{2}$(V^(t)+U^(t))	程序简单	未区分个体特点，修正合理性差
文献[14]	反馈修正	无	尊重决策者意愿	需决策者再次参与且共识达成难度大
文献[37]	反馈获得综合修正系数进行反馈修正	V^(t+1)=ηV^(t)+(1-η)U^(t), η=εη^s+(1-ε)η^o	综合修正系数融合了主观、客观因素, 决策更加合理, 可确保共识达成	需决策者再次参与
本文方法	由综合权重系数进行直接修正	V^(t+1)=p(λ)V^(t)+(1-p(λ))U^(t)	综合权重融合了主观、客观因素, 决策更加合理, 共识达成速度快	-

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