基于前景理论和区间数的应急物流供应商评价

doi:10.16265/j.cnki.issn1003-3033.2025.02.0534

摘要/Abstract

摘要：

为有效评价应急物流供应商的优劣,从供应商评价出发,结合前景理论和区间数提出应急物流供应商评价方法。首先,根据应急物流的特点,从快速响应能力、成本控制能力、产品质量、交货服务、企业内外部条件和柔性需求6个维度构建应急物流供应商评价指标体系;然后,在应急物流供应商评价中引入区间数,提出基于前景理论和区间数的评价方法,利用Jaccard相似系数定义区间数相似度,将与剩余方案相似度之和的最大值对应方案的属性评价值作为该属性的价值函数参考点,并利用离差最大化理论构造基于区间数相似度的多属性决策权重优化模型,得到属性赋权;最后,将价值函数归一化来扩大方案区分度,根据得到的权重函数和价值函数计算各方案的前景价值,并进行方案优劣排序。研究结果表明:文中评价方法计算的最优与最劣供应商前景价值差值为0.383 8,而以统计推断原理计算的前景价值差值为0.085 6,两者相差0.298 2,表明该评价方法扩大了方案间的区分度,能够帮助决策者实现有效决策。

关键词: 前景理论, 区间数, 应急物流, 供应商评价, 参考点, 相似度

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

中图分类号:

X913.3安全控制论

卫耀文, 戴宏, 赵舒宁. 基于前景理论和区间数的应急物流供应商评价[J]. 中国安全科学学报, 2025, 35(2): 236-243.

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.

图/表 12

表1

应急物流供应商评价指标体系

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

表1

表2

决策矩阵

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]

表2

表3

标准型决策矩阵

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]

表3

表4

属性C1下各供应商的相似度矩阵

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

表4

表5

属性C2下各供应商的相似度矩阵

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

表5

表6

属性C3下各供应商的相似度矩阵

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

表6

表7

表8

表9

表10

归一化价值矩阵

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

表10

表11

权重矩阵

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

表11

图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