考虑“因素-阶段”双重关联的复杂装备交付风险动态评估方法

doi:10.16381/j.cnki.issn1003-207x.2022.1330

摘要/Abstract

摘要：

研究复杂装备集成供应链交付风险动态评估模型。针对复杂装备交付环节特点，构建了交付风险影响因素体系；以概率语言形式表达决策者偏好，提出了考虑动态参照点和损失厌恶系数的前景理论分析模型；针对“因素-阶段”的双重关联性，构建了基于双重Choquet积分的优化模型；基于某航空制造企业的调研数据验证了方法的可行性。本研究可支持复杂装备交付风险管控方案的决策，为交付风险管控提供理论支撑。

关键词: 复杂装备交付风险, 动态评估, 因素-阶段双重关联性, 概率语言术语

Abstract:

The delivery link of the integrated supply chain of complex equipment is the key to the transformation of national defense combat power. Due to the complex and changeable delivery environment and the complex product technology， risks in the delivery process are frequent， and how to effectively identify and control risks has become a research difficulty in academia and industry. To solve this problem， the dynamic model of complex equipment delivery risk is studied under which delivery risk factors and stages （factors-stages） are related. According to the characteristics of complex equipment delivery， a criteria system affecting the delivery risk is constructed； the preference of decision-makers is expressed by using probabilistic linguistic terms， and a prospect theoretical analysis model that considers dynamic reference points and loss aversion coefficients are proposed； considering the dual correlation of factors-stages， risk measurement models based on Choquet integral are constructed. Finally， the feasibility of the method is verified based on the survey data of an aviation manufacturing enterprise. The results show that：（1） Due to poor technical stability， the delivery risk level of new complex equipment is higher than other types of risks；（2） Knowing customer needs in the early stage can help enterprises to resolve some risks in advance， helping to shorten the delivery cycle and improve customer satisfaction；（3） There is an interactive relationship between delivery risk， and enterprises need to focus on related risks when conducting risk investigation. It can support the decision-making of complex equipment delivery risk management and control schemes， continuously enhance the core competitiveness of enterprises， and provide a specific theoretical basis and practical value for enterprise delivery risk management and control.

Key words: delivery risk of complex equipment, dynamic model, dual correction of factors-stages, probabilistic linguistic terms

中图分类号:

C934

张思琪, 朱建军. 考虑“因素-阶段”双重关联的复杂装备交付风险动态评估方法[J]. 中国管理科学, 2025, 33(5): 173-183.

Siqi Zhang, Jianjun Zhu. A Dynamic Assessment Method for Complex Equipment Delivery Risk Considering the Dual Correlation of “Factors - stages”[J]. Chinese Journal of Management Science, 2025, 33(5): 173-183.

图/表 10

表1

表2

第一阶段下复杂装备交付风险评估信息期望值"

期望值	$g 1$	$g 2$	$g 3$	$g 4$	$g 5$
$h 1$	0.8095	0.8000	0.8286	0.6349	0.3429
$h 2$	0.6667	0.6000	0.6429	0.8000	0.5000
$h 3$	0.6714	0.6032	0.5429	0.5429	0.3492
$h 4$	0.5238	0.6714	0.4857	0.4857	0.3571

表2

表3

第二阶段下复杂装备交付风险评估信息期望值"

期望值	$g 1$	$g 2$	$g 3$	$g 4$	$g 5$
$h 1$	0.4571	0.6000	0.4714	0.4714	0.3286
$h 2$	0.4714	0.4603	0.3429	0.4571	0.3143
$h 3$	0.3143	0.4857	0.3286	0.3286	0.3143
$h 4$	0.3143	0.4571	0.3429	0.3143	0.2143

表3

表4

第一阶段下复杂装备交付风险的前景值"

前景值	$g 1$	$g 2$	$g 3$	$g 4$	$g 5$
$h 1$	0.0000	0.0000	0.0000	0.7145	1.0000
$h 2$	0.6923	1.0000	0.7269	0.0000	0.0000
$h 3$	0.6576	0.9914	0.9077	0.8990	0.9780
$h 4$	1.0000	0.7945	1.0000	1.0000	0.9502

表4

表5

第二阶段下复杂装备交付风险的前景值"

前景值	$g 1$	$g 2$	$g 3$	$g 4$	$g 5$
$h 1$	0.1093	0.0000	0.0000	0.0000	0.0000
$h 2$	0.0000	0.9881	0.9464	0.1255	0.2686
$h 3$	1.0000	0.8897	1.0000	0.9474	0.2686
$h 4$	1.0000	1.0000	0.9464	1.0000	1.0000

表5

图1

图2

图3

图4

图5

参考文献 24

1	Tian J H， Han D Y， Li M D， et al. A multi-source information transfer learning method with subdomain adaptation for cross-domain fault diagnosis［J］. Knowledge-Based Systems， 2022， 243： 108466.
2	Chen X N， Huang J， Yi M X. Cost estimation for general aviation aircrafts using regression models and variable importance in projection analysis ［J］. Journal of Cleaner Production， 2020， 256： 120648.
3	Shi Q， Zhou Y K， Xiao C， et al. Delivery risk analysis within the context of program management using fuzzy logic and DEA： A China case study ［J］. International Journal of Project Management， 2014， 32（2）： 341-349.
4	Bypaneni S P K， Tran D Q. Empirical identification and evaluation of risk in highway project delivery methods ［J］. Journal of Management in Engineering， 2018， 34（3）： 04018007.
5	Rodriguez R M， Martinez L， Herrera F. Hesitant fuzzy linguistic term sets for decision making［J］. IEEE Transactions on Fuzzy Systems， 2012， 20（1）： 109-119.
6	Pang Q， Wang H， Xu Z S. Probabilistic linguistic term sets in multi-attribute group decision making ［J］. Information Sciences， 2016， 369：128-143.
7	Huang J， Liu H C， Duan C Y， et al. An improved reliability model for FMEA using probabilistic linguistic term sets and TODIM method ［J］. Annals of Operations Research， 2022， 312（1）： 235-258.
8	Liang Y Y， Ju Y B， Qin J D， et al. Multi-granular linguistic distribution evidential reasoning method for renewable energy project risk assessment ［J］. Information Fusion， 2021， 65：147-164.
9	Liu P D， Li Y. An improved failure mode and effect analysis method for multi-criteria group decision-making in green logistics risk assessment ［J］. Reliability Engineering & System Safety， 2021， 215： 107826.
10	Wang W Z， Liu X W， Qin Y. A fuzzy Fine-Kinney-based risk evaluation approach with extended Multimoora method based on Choquet integral ［J］. Computers & Industrial Engineering， 2018， 125：111-123.
11	杨娜，廖貅武，雷宏振. 考虑属性关联的两阶段获胜者确定模型［J］.中国管理科学，2019， 27（10）：148-158.
	Yang N， Liao X W， Lei H Z. Two stage winner determination approach with interacting atributes［J］. Chinese Journal of Management Science，2019， 27（10）：148-158.
12	Guo X X， Ji J， Khan F， et al. A novel fuzzy dynamic Bayesian network for dynamic risk assessment and uncertainty propagation quantification in uncertainty environment ［J］. Safety Science， 2021， 141： 105285.
13	Qian H， Zhang R， Zhang Y J. Dynamic risk assessment of natural environment based on dynamic Bayesian network for key nodes of the arctic northwest passage ［J］. Ocean Engineering， 2020， 203： 107205.
14	Zhao M W， Wei G W， Wei C， et al. Pythagorean fuzzy TODIM method based on the cumulative prospect theory for MAGDM and its application on risk assessment of science and technology projects ［J］. International Journal of Fuzzy Systems， 2021， 23（4）：1027-1041.
15	Wang W Z， Liu X W， Qin Y， et al. A risk evaluation and prioritization method for FMEA with prospect theory and Choquet integral［J］. Safety Science， 2018， 110： 152-163.
16	Chen W， Yang B， Liu Y. An integrated QFD and FMEA approach to identify risky components of products［J］. Advanced Engineering Informatics， 2022， 54： 101808.
17	Wu X L， Liao H C， Xu Z S， et al. Probabilistic linguistic MULTIMOORA： A multicriteria decision making method based on the probabilistic linguistic expectation function and the improved Borda rule ［J］. IEEE Transactions on Fuzzy Systems， 2018， 26（6）：3688-3702.
18	Wang W Z， Ding L， Liu X W， et al. An interval 2-Tuple linguistic Fine-Kinney model for risk analysis based on extended ORESTE method with cumulative prospect theory ［J］. Information Fusion， 2022， 78：40-56.
19	Song W， Zhu J J， Wang H H， et al. Multistage risk assessment of direct delivery business from local oil refineries in Sinopec Group based on normal cloud model ［J］. International Journal of Production Research， 2020， 58（18）：5624-5650.
20	Gong Z W， Guo W W， Slowinski R. Transaction and interaction behavior-based consensus model and its application to optimal carbon emission reduction［J］. Omega，2021，104：102491.
21	Tversky A， Kahneman D. Advances in prospect theory： Cumulative representation of uncertainty［M］// Arló-Costa H， Hendricks V F， van Benthem J. Readings in Formal Epistemology： Sourcebook. Cham： Springer International Publishing， 2016： 493-519.
22	Song W， Zhu J J. Three-reference-point decision-making method with incomplete weight information considering independent and interactive characteristics ［J］. Information Sciences， 2019， 503：148-168.
23	李美娟，袁宁，徐林明. 基于正交投影法的动态评价方法［J］. 中国管理科学，2020，28（12）：208-219.
	Lei M J， Yuan N， Xu L M. Dynamic evaluation method based on vertical projection method［J］. Chinese Journal of Management Science， 2020，28（12）：208-219.
24	徐林明，李美娟，卢锦呈. 考虑决策者偏好的均衡接近度灰关联改进TOPSIS动态评价方法及其应用研究［J］. 中国管理科学， 2023，31（3）：251-258.
	Xu L M， Lei M J， Lu J C. Dynamic evaluation method based on grey correlational analysis by the method of balance and approach and improved TOPSIS considering the preference of the decision maker［J］. Chinese Journal of Management Science， 2023，31（3）：251-258.

一级指标	二级指标	含义及描述
交付人员风险(g₁)	操作规范性差(g₁₁)	操作规范不统一，复杂装备表面易出现划伤、掉漆等情况
	安全意识薄弱(g₁₂)	高空作业人员有坠落的风险，地面作业人员有被高空抛物砸伤的风险
	随机工具配发错误(g₁₃)	不同装备的随机工具略有差异，工作人员对其认知不足，容易错误配发
	随机设备等配套资料缺失(g₁₄)	工作人员检查不仔细，配套资料易缺失
交付设备风险(g₂)	随机设备等延期交付(g₂₁)	随机设备等可能存在配套不齐全、过期等情况
	零部件等存在质量缺陷(g₂₂)	各厂家对零部件质量管控不严格
	复杂装备技术状态不稳定(g₂₃)	装备设计更改频繁，改装工作造成的衍生故障较多
交付管理风险(g₃)	检查所需资源分配不合理(g₃₁)	检查所需的场地、人员、工具及设备不足
交付管理风险(g₃)	记录性文件填写不规范(g₃₂)	对记录性文件填写等要求贯彻不到位，文件填写/更改不正确
交付标准风险(g₄)	喷漆标准不一致(g₄₁)	客户不认同企业现行喷漆标准
交付标准风险(g₄)	交付检查标准不一致(g₄₂)	客户和企业参照的检查标准不一致
交付环境风险(g₅)	不良气候条件(g₅₁)	在露天开放环境下工作，遇雷雨等不良天气
交付环境风险(g₅)	其他交付风险(g₅₂)	如在现场试用结束后发现有漆面损伤等