Evaluation of emergency logistics suppliers based on prospect theory and interval numbers

doi:10.16265/j.cnki.issn1003-3033.2025.02.0534

Abstract

Abstract:

To effectively evaluate the performance of emergency logistics suppliers, a method for evaluating emergency logistics suppliers was proposed based on supplier evaluation, incorporating prospect theory and interval numbers. Firstly, based on the characteristics of emergency logistics, an evaluation index system for emergency logistics suppliers was proposed from six dimensions: rapid response capability, cost control capability, product quality, delivery service, internal and external conditions of the enterprise, and flexible demand. Then, interval numbers were introduced into the evaluation of emergency logistics suppliers, and an evaluation method based on prospect theory and interval numbers was proposed. The Jaccard similarity coefficient was used to define the similarity of interval number. The maximum sum of similarity with the remaining solutions was used to determine the reference point of the value function for the attribute evaluation value of the corresponding solution. The deviation maximization theory was used to construct a multi-attribute decision weight optimization model based on interval number similarity, from which attribute weights were obtained. Finally, the value function was normalized to expand the scheme discrimination. The prospect value of each scheme was calcuted based on the obtained weight function and value function, and the advantages and disadvantages of the scheme were ranked. The research results indicate that the difference in prospect values between the optimal and worst suppliers calculated using the evaluation method is 0.383 8, while the prospect value difference calculated using statistical inference principles is 0.085 6. The difference of 0.298 2 shows that the proposed evaluation method expands the differentiation between options, helping decision-makers achieve effective decisions.

Key words: emergency logistic, prospect theory, supplier evaluation, number of intervals, reference point, similarity

CLC Number:

X913.3安全控制论

WEI Yaowen, DAI Hong, ZHAO Shuning. Evaluation of emergency logistics suppliers based on prospect theory and interval numbers[J]. China Safety Science Journal, 2025, 35(2): 236-243.

Figures/Tables 12

Table 1

Emergency logistics supplier evaluation index system

目标层	一级指标	二级指标
应急物流供应商评价指标体系	快速响应能力 $C 1$	物资供给能力
		物资生产能力
		物资储备能力
	成本控制能力 $C 2$	运输成本
		产品成本
		库存成本
	产品质量 $C 3$	产品耐用度
		产品合格率
		产品退货率
	交货服务 $C 4$	交货时间
		订单完成率
		按时交货率
	企业内外部条件 $C 5$	地理位置
	企业内外部条件 $C 5$	信息化水平
	柔性需求 $C 6$	数量柔性
		时间柔性
		产品柔性

Table 1

Table 2

Decision matrix

评价值	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$
$A 1$	[5,6]	[6,8]	[6,7]	[4,6]	[7,8]	[8,10]
$A 2$	[6,8]	[5,7]	[8,9]	[7,8]	[4,7]	[7,8]
$A 3$	[5,7]	[6,7]	[8,10]	[7,9]	[5,7]	[6,7]
$A 4$	[8,10]	[5,6]	[4,7]	[5,7]	[6,8]	[4,7]
$A 5$	[8,10]	[6,8]	[5,6]	[6,9]	[7,8]	[5,8]

Table 2

Table 3

Standard decision matrix

评价值	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$
$A 1$	[0.268,0.410]	[0.371,0.636]	[0.338,0.489]	[0.227,0.454]	[0.411,0.605]	[0.443,0.725]
$A 2$	[0.321,0.547]	[0.309,0.557]	[0.451,0.629]	[0.397,0.605]	[0.235,0.529]	[0.388,0.580]
$A 3$	[0.268,0.479]	[0.371,0.557]	[0.451,0.698]	[0.397,0.680]	[0.294,0.529]	[0.332,0.508]
$A 4$	[0.428,0.684]	[0.309,0.477]	[0.225,0.489]	[0.284,0.529]	[0.352,0.605]	[0.222,0.508]
$A 5$	[0.428,0.684]	[0.371,0.636]	[0.282,0.419]	[0.340,0.680]	[0.411,0.605]	[0.277,0.580]

Table 3

Table 4

Similarity matrix of suppliers under attribute C1

相似度	$A 1$	$A 2$	$A 3$	$A 4$	$A 5$
$A 1$	1	0.319	0.676	0	0
$A 2$	0.319	1	0.563	0.327	0.327
$A 3$	0.676	0.563	1	0.121	0.121
$A 4$	0	0.327	0.121	1	1
$A 5$	0	0.327	0.121	1	1

Table 4

Table 5

Similarity matrix of suppliers under attribute C2

相似度	$A 1$	$A 2$	$A 3$	$A 4$	$A 5$
$A 1$	1	0.568	0.701	0.326	1
$A 2$	0.568	1	0.751	0.679	0.568
$A 3$	0.701	0.751	1	0.430	0.701
$A 4$	0.326	0.679	0.430	1	0.326
$A 5$	1	0.568	0.701	0.326	1

Table 5

Table 6

Similarity matrix of suppliers under attribute C3

相似度	$A 1$	$A 2$	$A 3$	$A 4$	$A 5$
$A 1$	1	0.131	0.106	0.572	0.391
$A 2$	0.131	1	0.718	0.095	0
$A 3$	0.106	0.718	1	0.081	0
$A 4$	0.572	0.095	0.081	1	0.521
$A 5$	0.391	0	0	0.521	1

Table 6

Table 7

Table 8

Table 9

Table 10

Normalized value matrix

价值函数值	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$
$A 1$	-1	0	0.171	-1	0.097	1
$A 2$	0	-0.395	0.769	-0.176	-1	0.297
$A 3$	-0.397	-0.261	1	0.032	-0.514	-0.476
$A 4$	0.597	-1	0	-0.452	0	-0.476
$A 5$	0.597	0	-0.257	0	0.097	0

Table 10

Table 11

Weight matrix

权重	$C 1$	$C 2$	$C 3$	$C 4$	$C 5$	$C 6$
$A 1$	0.257	0.219	0.248	0.217	0.219	0.254
$A 2$	0.261	0.208	0.248	0.217	0.208	0.254
$A 3$	0.257	0.208	0.248	0.227	0.208	0.249
$A 4$	0.261	0.208	0.248	0.217	0.219	0.249
$A 5$	0.261	0.219	0.241	0.227	0.219	0.254

Table 11

Fig.1

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相似度	A₁	A₂	A₃	A₄	A₅
A₁	1	0.150	0.125	0.562	0.250
A₂	0.150	1	0.733	0.412	0.611
A₃	0.125	0.733	1	0.333	0.833
A₄	0.562	0.412	0.333	1	0.476
A₅	0.250	0.611	0.833	0.476	1

相似度	A₁	A₂	A₃	A₄	A₅
A₁	1	0.319	0.380	0.767	1
A₂	0.319	1	0.800	0.478	0.319
A₃	0.380	0.800	1	0.568	0.380
A₄	0.767	0.478	0.568	1	0.767
A₅	1	0.319	0.380	0.767	1

相似度	A₁	A₂	A₃	A₄	A₅
A₁	1	0.406	0.165	0.128	0.306
A₂	0.406	1	0.484	0.335	0.635
A₃	0.165	0.484	1	0.613	0.578
A₄	0.128	0.335	0.613	1	0.643
A₅	0.306	0.635	0.578	0.643	1