基于直觉语言粗糙CoCoSo-H的废旧动力电池回收技术选择模型

doi:10.16381/j.cnki.issn1003-207x.2021.0319

Abstract

Abstract:

How to select the optimal spent power battery recycling technology（SPBRT） is an important group decision-making problem in the recycling management of waste power battery. Firstly， the intuitionistic linguistic rough Heronian mean （ILRHM） and intuitionistic linguistic rough geometric Heronian mean （ILRGHM） operators and their weighted forms are defined based on the intuitionistic linguistic rough numbers （ILRNs） in this paper， and the related some properties and special cases are discussed. Then， a multi-attribute intuitionistic linguistic rough group decision-making model is proposed， which includes the combination weights of attributes are determined by the decision maker’s evaluation information and the deviation maximization principle， the traditional CoCoSo method is improved by using intuitionistic linguistic rough weighted Heronian mean （ILRWHM） and intuitionistic linguistic rough weighted geometric Heronian mean （ILRWGHM）， and evaluate alternatives and make the optimal decision considering the decision maker’s risk preference. This method （1） can capture the correlation between attributes，（2） can realize the balance between optimistic and pessimistic decision-making attitudes of decision makers，（3） introduces the risk preference coefficient of decision makers to make alternative ordering more flexible， and （4） can detect the influence of parameters on decision results through the change analysis of decision-making parameters and has robustness. Finally， in order to verify the effectiveness and rationality of the proposed method， SPBRT selection is taken as an example to demonstrate the advantages of this method through effect of parameters and comparative analysis with other methods.

Key words: intuitionistic linguistic rough number, intuitionistic linguistic rough Heronian mean aggregation operators, ILRCoCoSo-H, SPBRT selection, multi-attribute group decision making

CLC Number:

C934

Haolun Wang. Spent Power Battery Recycling Technology Selection Based on Integrated CoCoSo and Heronian Mean with Intuitionistic Linguistic Rough Number[J]. Chinese Journal of Management Science, 2024, 32(5): 24-37.

Figures/Tables 12

方案	$[C ? i (1)]$	$[C ? i (2)]$	$C i (1)$	$C i (2)$
A₁	[<l_2.204,(0.399,0.411)>,<h_2.274,(0.464,0.372)>]	[<l_1.544,(0.543,0.272)>,<h_1.601,(0.605,0.253)>]	1.165	1.031
A₂	[<l_3.872,(0.449,0.391)>,<h_3.950,(0.461,0.342)>]	[<l_2.749,(0.599,0.246)>,<h_2.824,(0.603,0.215)>]	2.129	1.910
A₃	[<l_3.240,(0.442,0.390)>,<h_3.428,(0.435,0.370)>]	[<l_2.347,(0.590,0.250)>,<h_2.506,(0.579,0.244)>]	1.765	1.623
A₄	[<l_2.443,(0.448,0.425)>,<h_1.753,(0.435,0.376)>]	[<l_1.839,(0.593,0.282)>,<h_1.438,(0.582,0.240)>]	1.089	1.085

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文献	决策类型	数值类型	属性权重	是否考虑属性交互	是否考虑风险偏好	应用领域
Erceg等(2019)^[28]	MAGDM	区间粗糙数	客观	否	否	库存管理
Wen等(2019)^[29]	MAGDM	犹豫模糊语言数	组合	否	否	3PL服务
Wen等(2019)^[30]	MAGDM	概率语言数	主观	否	否	医药冷链
Yazdani等(2019)^[20]	MCDM	精确数	主观	否	否	物流供应商
Yazdani等(2019)^[26]	MAGDM	灰色数	主观	否	否	供应商
Peng等(2019)^[20]	MCDM	勾股模糊数	组合	否	否	5G产业
Yazdani等(2020)^[27]	MAGDM	粗糙数	客观	否	否	选址
Ecer和Pamucar(2020)^[21]	MAGDM	模糊数	主观	是	否	可持续供应商
Zolfani和Pamucar(2020)^[25]	MCDM	灰色数	客观	否	否	选址
Liao和Pamucar(2020)^[23]	MAGDM	勾股模糊数	组合	否	否	冷链分拨中心
Peng和Huang(2020)^[24]	MCDM	广义正交模糊数	组合	否	否	金融风险
本文	MAGDM	ILRN	组合	是	是	SPBRT

方案	SPBRT	说明
A₁	火法工艺	在预处理后在高温下回收金属，特点是能耗高、操作简单和产物单一等
A₂	湿法工艺	预处理后主要通过无机酸或有机酸进行酸浸，分离浸出液中金属元素。特点成本低、效率受溶剂种类、浓度、温度等因素影响
A₃	生物浸出	利用微生物代谢或微生物产酸工艺提取有价值金属，特点是成本低、污染小、可重复
A₄	电化学萃取	利用金属离子在水相和有机相中的不均匀分布进行分离，特点是绿色、高效、能耗高

专家	C₁	C₂	C₃	C₄	C₅	C₆	C₇
e¹	<l₅,(0.4,0.3)>	<l₆,(0.5,0.5)>	<l₅,(0.6,0.3)>	<l₄,(0.7,0.1)>	<l₅,(0.2,0.5)>	<l₅,(0.5,0.5)>	<l₅,(0.3,0.3)>
e²	<l₄,(0.7,0.2)>	<l₆,(0.6,0.2)>	<l₅,(0.5,0.3)>	<l₃,(0.5,0.4)>	<l₅,(0.4,0.2)>	<l₅,(0.6,0.2)>	<l₆,(0.5,0.1)>
e³	<l₅,(0.6,0.3)>	<l₆,(0.3,0.5)>	<l₅,(0.4,0.6)>	<l₄,(0.3,0.5)>	<l₅,(0.4,0.4)>	<l₄,(0.2,0.8)>	<l₅,(0.4,0.5)>
e⁴	<l₅,(0.4,0.5)>	<l₆,(0.6,0.1)>	<l₅,(0.3,0.2)>	<l₃,(0.8,0.2)>	<l₄,(0.6,0.1)>	<l₆,(0.4,0.2)>	<l₆,(0.8,0.2)>
e⁵	<l₄,(0.8,0.1)>	<l₆,(0.4,0.5)>	<l₄,(0.6,0.1)>	<l₄,(0.6,0.1)>	<l₅,(0.5,0.4)>	<l₅,(0.6,0.4)>	<l₅,(0.4,0.2)>

方案	专家	C₁	C₂	C₃	C₄	C₅	C₆	C₇
A₁	e¹	<l₁,(0.5,0.4)>	<l₆,(0.5,0.2)>	<l₅,(0.3,0.3)>	<l₃,(0.4,0.6)>	<l₀,(0.5,0.4)>	<l₄,(0.3,0.4)>	<l₁,(0.4,0.3)>
	e²	<l₁,(0.6,0.2)>	<l₅,(0.3,0.5)>	<l₆,(0.5,0.2)>	<l₁,(0.5,0.3)>	<l₁,(0.4,0.3)>	<l₅,(0.8,0.1)>	<l₂,(0.5,0.4)>
	e³	<l₂,(0.3,0.5)>	<l₅,(0.8,0.1)>	<l₆,(0.3,0.5)>	<l₂,(0.6,0.2)>	<l₄,(0.3,0.2)>	<l₄,(0.4,0.2)>	<l₂,(0.5,0.3)>
	e⁴	<l₁,(0.6,0.3)>	<l₄,(0.4,0.2)>	<l₅,(0.8,0.1)>	<l₃,(0.4,0.5)>	<l₁,(0.5,0.3)>	<l₃,(0.7,0.2)>	<l₁,(0.4,0.5)>
	e⁵	<l₀,(0.6,0.3)>	<l₅,(0.3,0.3)>	<l₆,(0.3,0.2)>	<l₃,(0.7,0.3)>	<l₂,(0.5,0.2)>	<l₃,(0.4,0.4)>	<l₀,(0.3,0.5)>
A₂	e¹	<l₄,(0.8,0.1)>	<l₄,(0.6,0.1)>	<l₅,(0.4,0.4)>	<l₅,(0.5,0.4)>	<l₄,(0.6,0.2)>	<l₆,(0.3,0.2)>	<l₄,(0.8,0.2)>
	e²	<l₅,(0.3,0.4)>	<l₄,(0.4,0.3)>	<l₆,(0.5,0.3)>	<l₅,(0.3,0.4)>	<l₆,(0.4,0.3)>	<l₅,(0.3,0.4)>	<l₅,(0.6,0.3)>
	e³	<l₄,(0.8,0.2)>	<l₆,(0.5,0.3)>	<l₅,(0.8,0.1)>	<l₄,(0.3,0.4)>	<l₆,(0.5,0.3)>	<l₅,(0.4,0.6)>	<l₆,(0.5,0.2)>
	e⁴	<l₄,(0.6,0.2)>	<l₃,(0.7,0.1)>	<l₄,(0.4,0.3)>	<l₅,(0.3,0.3)>	<l₄,(0.4,0.2)>	<l₂,(0.7,0.3)>	<l₃,(0.7,0.3)>
	e⁵	<l₄,(0.3,0.2)>	<l₄,(0.2,0.5)>	<l₄,(0.5,0.3)>	<l₅,(0.6,0.3)>	<l₄,(0.3,0.4)>	<l₆,(0.4,0.2)>	<l₅,(0.4,0.4)>
A₃	e¹	<l₂,(0.7,0.2)>	<l₅,(0.8,0.1)>	<l₃,(0.5,0.1)>	<l₄,(0.6,0.2)>	<l₅,(0.3,0.3)>	<l₅,(0.5,0.5)>	<l₃,(0.5,0.3)>
	e²	<l₃,(0.4,0.4)>	<l₅,(0.5,0.5)>	<l₃,(0.4,0.5)>	<l₃,(0.4,0.4)>	<l₅,(0.8,0.2)>	<l₄,(0.4,0.5)>	<l₄,(0.3,0.2)>
	e³	<l₄,(0.3,0.5)>	<l₄,(0.3,0.6)>	<l₄,(0.3,0.2)>	<l₆,(0.3,0.2)>	<l₅,(0.3,0.6)>	<l₄,(0.6,0.2)>	<l₅,(0.4,0.3)>
	e⁴	<l₃,(0.7,0.1)>	<l₄,(0.4,0.5)>	<l₄,(0.6,0.2)>	<l₄,(0.6,0.3)>	<l₅,(0.8,0.1)>	<l₂,(0.6,0.1)>	<l₅,(0.6,0.3)>
	e⁵	<l₁,(0.7,0.2)>	<l₃,(0.5,0.1)>	<l₃,(0.5,0.5)>	<l₄,(0.4,0.3)>	<l₅,(0.3,0.4)>	<l₃,(0.5,0.2)>	<l₄,(0.3,0.4)>
A₄	e¹	<l₃,(0.4,0.4)>	<l₀,(0.6,0.3)>	<l₁,(0.6,0.1)>	<l₄,(0.7,0.2)>	<l₂,(0.7,0.2)>	<l₂,(0.5,0.1)>	<l₀,(0.6,0.2)>
	e²	<l₅,(0.8,0.2)>	<l₁,(0.5,0.4)>	<l₂,(0.5,0.2)>	<l₅,(0.5,0.5)>	<l₁,(0.6,0.3)>	<l₂,(0.7,0.2)>	<l₁,(0.5,0.4)>
	e³	<l₅,(0.6,0.3)>	<l₆,(0.3,0.5)>	<l₂,(0.4,0.3)>	<l₃,(0.4,0.5)>	<l₂,(0.4,0.3)>	<l₅,(0.3,0.4)>	<l₁,(0.3,0.5)>
	e⁴	<l₅,(0.4,0.3)>	<l₁,(0.4,0.2)>	<l₅,(0.3,0.4)>	<l₂,(0.6,0.4)>	<l₂,(0.7,0.3)>	<l₃,(0.4,0.5)>	<l₁,(0.6,0.1)>
	e⁵	<l₄,(0.3,0.2)>	<l₁,(0.4,0.5)>	<l₃,(0.4,0.4)>	<l₃,(0.5,0.4)>	<l₁,(0.5,0.4)>	<l₂,(0.3,0.5)>	<l₁,(0.7,0.3)>

方案	K_ia	K_ib	K_ic	K_i	排序
A₁	0.186	2.070	0.544	1.527	3
A₂	0.342	3.807	1	2.809	1
A₃	0.287	3.195	0.839	2.357	2
A₄	0.184	2.052	0.538	1.513	4

Spent Power Battery Recycling Technology Selection Based on Integrated CoCoSo and Heronian Mean with Intuitionistic Linguistic Rough Number

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参数	K_i 值	排序
s=0,t=1	K₁=1.626,K₂=3.434,K₃=3.022,K₄=1.323	A₂>A₃>A₁>A₄
s=1,t=0	K₁=1.532,K₂=2.617,K₃=2.050,K₄=1.819	A₂>A₃>A₄>A₁
s=1,t=1	K₁=1.527,K₂=2.809,K₃=2.357,K₄=1.513	A₂>A₃>A₁>A₄
s=1,t=3	K₁=1.600,K₂=3.082,K₃=2.639,K₄=1.387	A₂>A₃>A₁>A₄
s=1,t=5	K₁=1.684,K₂=3.222,K₃=2.754,K₄=1.343	A₂>A₃>A₁>A₄
s=3,t=1	K₁=1.553,K₂=2.611,K₃=2.094,K₄=1.756	A₂>A₃>A₄>A₁
s=3,t=3	K₁=1.549,K₂=2.740,K₃=2.282,K₄=1.556	A₂>A₃>A₄>A₁
s=3,t=5	K₁=1.614,K₂=2.897,K₃=2.426,K₄=1.483	A₂>A₃>A₁>A₄
s=5,t=1	K₁=1.582,K₂=2.566,K₃=2.007,K₄=1.904	A₂>A₃>A₄>A₁
s=5,t=5	K₁=1.587,K₂=2.739,K₃=2.257,K₄=1.576	A₂>A₃>A₁>A₄

方法	计算结果	排序
ILWAA^[36]	a₁=3.232, a₂=6.044,a₃=4.847, a₄=3.120	A₂>A₃>A₁>A₄
ILWGA^[37]	a₁=0.000, a₂=5.453,a₃=4.156, a₄=0.000	A₂>A₃>A₁=A₄
ILRWA^[3]	a₁=3.544, a₂=6.797,a₃=5.455, a₄=3.510	A₂>A₃>A₁>A₄
ILRWGA^[3]	a₁=0.001, a₂=0.045,a₃=0.012, a₄=0.001	A₂>A₃>A₁≈A₄
TOPSIS^[17]	ζ₁=0.311, ζ₂=0.809, ζ₃=0.463, ζ₄=0.233	A₂>A₃>A₁>A₄
CoCoSo^[20]	K₁=1.821, K₂=2.634,K₃=2.136, K₄=1.722	A₂>A₃>A₁>A₄
本文方法	K₁=1.527,K₂=2.809,K₃=2.357,K₄=1.513	A₂>A₃>A₁>A₄

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