Article(id=1148106715444277764, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1148106709542892487, articleNumber=1003-3033(2025)04-0120-07, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.04.1080, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1732464000000, receivedDateStr=2024-11-25, revisedDate=1739808000000, revisedDateStr=2025-02-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1751659571753, onlineDateStr=2025-07-05, pubDate=1745769600000, pubDateStr=2025-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1751659571753, onlineIssueDateStr=2025-07-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1751659571753, creator=13701087609, updateTime=1751659571753, updator=13701087609, issue=Issue{id=1148106709542892487, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='4', pageStart='1', pageEnd='264', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=0, createTime=1751659570346, creator=13701087609, updateTime=1757560692417, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1172857809499730113, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1148106709542892487, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1172857809499730114, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1148106709542892487, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=120, endPage=126, ext={EN=ArticleExt(id=1149757847262643013, articleId=1148106715444277764, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Simulation of cluster supply chain network recovery strategy under external risk shock, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To prevent the impact of external risks on cluster supply chain network,the propagation mechanism of external risks in cluster supply chain network was explored based on cascade failure theory. Feasible strategies to curb the spread of risks in cluster supply chain networks under external risk impacts were also explored through numerical simulation using Python. The results indicate that implementing risk tolerance enhancement strategies in important enterprises cannot prevent the eventual collapse of the network,but can effectively slow down the speed of risk propagation and reduce the impact of each step in the risk propagation process. However,the implementation of risk mitigation strategies by important enterprises can effectively prevent the spread of risks in the network and prevent the collapse of clustered supply chain networks. The implementation of risk tolerance enhancement strategies in the global supply chain can effectively prevent the spread of risks in the network and prevent network collapse. Although establishing a supply chain risk sharing mechanism cannot prevent cluster supply chain network paralysis under deliberate attacks,it can effectively reduce the number of failed nodes under random attacks and prevent cluster supply chain network paralysis.

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为预防外部风险对集群式供应链网络造成的冲击,基于级联失效理论探索外部风险在集群式供应链网络中的传播机制,并使用Python进行数值仿真以探索遏制外部风险冲击下集群式供应链网络风险传播的可行策略。结果表明:重要企业实施风险耐受性提升策略不能阻止网络最终崩溃,但可有效延缓风险传播的速度并减轻每一步风险传播过程中的影响程度;重要企业实施风险遏止能力提升策略可有效阻止风险在网络中传播,防止集群式供应链网络瘫痪;供应链全局实施风险耐受性提升策略可有效阻止风险在网络中的蔓延并防止网络的崩溃;建立供应链风险分担机制虽然不能阻止蓄意攻击下集群式供应链网络瘫痪,但可有效减少随机攻击下失效节点数并防止集群式供应链网络瘫痪。

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田杰鑫 (1998—),女,湖北宜昌人,博士研究生,主要研究方向为产业链和供应链。E-mail:

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外部风险冲击下集群式供应链网络恢复策略仿真
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田杰鑫 1 , 何宇 副教授 2 , 覃朝晖 教授 2
中国安全科学学报 | 安全工程技术 2025,35(4): 120-126
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中国安全科学学报 | 安全工程技术 2025, 35(4): 120-126
外部风险冲击下集群式供应链网络恢复策略仿真
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田杰鑫1 , 何宇 副教授2, 覃朝晖 教授2
作者信息
  • 1 中国地质大学(武汉) 经济管理学院,湖北 武汉 430078
  • 2 三峡大学 经济与管理学院,湖北 宜昌 443002
  • 田杰鑫 (1998—),女,湖北宜昌人,博士研究生,主要研究方向为产业链和供应链。E-mail:

Simulation of cluster supply chain network recovery strategy under external risk shock
Jiexin TIAN1 , Yu HE2, Zhaohui QIN2
Affiliations
  • 1 School of Economics and Management,China University of Geosciences (Wuhan),Wuhan Hubei 430078,China
  • 2 College of Economics and Management,China Three Gorges University,Yichang Hubei 443002,China
出版时间: 2025-04-28 doi: 10.16265/j.cnki.issn1003-3033.2025.04.1080
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为预防外部风险对集群式供应链网络造成的冲击,基于级联失效理论探索外部风险在集群式供应链网络中的传播机制,并使用Python进行数值仿真以探索遏制外部风险冲击下集群式供应链网络风险传播的可行策略。结果表明:重要企业实施风险耐受性提升策略不能阻止网络最终崩溃,但可有效延缓风险传播的速度并减轻每一步风险传播过程中的影响程度;重要企业实施风险遏止能力提升策略可有效阻止风险在网络中传播,防止集群式供应链网络瘫痪;供应链全局实施风险耐受性提升策略可有效阻止风险在网络中的蔓延并防止网络的崩溃;建立供应链风险分担机制虽然不能阻止蓄意攻击下集群式供应链网络瘫痪,但可有效减少随机攻击下失效节点数并防止集群式供应链网络瘫痪。

外部风险  /  集群式供应链网络  /  恢复策略  /  级联失效  /  复杂网络

To prevent the impact of external risks on cluster supply chain network,the propagation mechanism of external risks in cluster supply chain network was explored based on cascade failure theory. Feasible strategies to curb the spread of risks in cluster supply chain networks under external risk impacts were also explored through numerical simulation using Python. The results indicate that implementing risk tolerance enhancement strategies in important enterprises cannot prevent the eventual collapse of the network,but can effectively slow down the speed of risk propagation and reduce the impact of each step in the risk propagation process. However,the implementation of risk mitigation strategies by important enterprises can effectively prevent the spread of risks in the network and prevent the collapse of clustered supply chain networks. The implementation of risk tolerance enhancement strategies in the global supply chain can effectively prevent the spread of risks in the network and prevent network collapse. Although establishing a supply chain risk sharing mechanism cannot prevent cluster supply chain network paralysis under deliberate attacks,it can effectively reduce the number of failed nodes under random attacks and prevent cluster supply chain network paralysis.

external risk  /  cluster supply chain network  /  recovery strategy  /  cascading failure  /  complex network
田杰鑫, 何宇 副教授, 覃朝晖 教授. 外部风险冲击下集群式供应链网络恢复策略仿真. 中国安全科学学报, 2025 , 35 (4) : 120 -126 . DOI: 10.16265/j.cnki.issn1003-3033.2025.04.1080
Jiexin TIAN, Yu HE, Zhaohui QIN. Simulation of cluster supply chain network recovery strategy under external risk shock[J]. China Safety Science Journal, 2025 , 35 (4) : 120 -126 . DOI: 10.16265/j.cnki.issn1003-3033.2025.04.1080
在新一轮科技革命和产业变革的背景下,大数据、云计算、人工智能等数字技术的快速发展,企业的生产链条纵向切割的趋势逐渐明显,从原来的由工序关系组成的单一供应链链条逐渐转变为从生产到流通的多层次、多维度交织融合的复杂供应链网络[1]。然而,在百年未有之大变局背景下,“逆全球化”、国际局部冲突和动荡加剧、全球突发性公共卫生事件频发使得全球产业链供应链遭受了巨大冲击。在此背景下,探索遏制外部风险冲击下集群式供应链网络风险传播的可行方案,对中国经济行稳致远具有重要的现实意义。
复杂网络理论因其对复杂系统中节点关联与动态演变的强大解析力,为剖析风险传播机制提供了有力工具。早期,复杂网络受到众多学者的关注。ALBERT等[2]探索了随机攻击和蓄意攻击2种攻击模式下复杂网络的鲁棒性,发现无标度网络在随机攻击下表现出极强的鲁棒性,但在蓄意攻击下表现出脆弱性。现实中很多网络都是错综复杂的,具有复杂网络的结构,一个或几个节点发生故障可能造成整个网络瘫痪[3]。级联故障可能发生在交通网络[4]、互联网[5]、电网[6]等现实网络中。运用级联失效理论探讨风险传播的研究主要聚焦于:①网络中某些节点失效后网络的拓扑结构以及网络的鲁棒性[7];②风险在级联失效模型下网络的传播过程[8]。在级联失效理论中应用最多的是负载容量模型,这个模型主要通过风险负荷和风险抵抗能力研究风险传播,当节点的风险负载超过其容量承受能力时,这些节点将触发相邻节点的潜在风险[9-10]。在防范网络级联失效导致严重的后果方面,有学者发现,只要网络中的负载分布更均匀,网络发生级联失效的范围就会变小[11]。王建伟等[12]提出保护度较高的节点或使节点负载分布相对均匀是防止级联失效的有效方法。崔慧霞等[13]指出,实行网络恢复机制时应优先考虑恢复使网络运营度增加大的节点或连边。
学者们围绕复杂网络和级联失效等方面进行了较为丰富的研究,但还存在以下不足:①未从级联失效的角度探索外部风险在集群式供应链网络中的传播机制;②现有文献对于防范网络级联失效仅停留在理论层面,较少通过数值仿真探讨恢复策略的作用。鉴于此,笔者拟基于复杂网络理论和级联失效理论,探索外部风险在集群式供应链网络中的传播机制;并利用Python多角度进行集群式供应链网络面对外部风险冲击后的恢复策略仿真,以期有利于遏制外部风险冲击下集群式供应链网络风险传播。
供应链网络是供应商、制造商、装配商、分销商以及零售商通过物流、信息流和资金流等方式建立的一种在战略、任务、资源和能力等方面存在相互依赖的一种供应-生产-销售网络。这种供应链网络仅存在一个核心企业,其本质上仍然属于链式结构,并没有与其他供应链网络建立联系,供应链网络中的各个企业专业化于各自的业务活动,围绕一个核心企业开展业务。
集群式供应链网络相较于一般供应链网络存在比较明显的区别。集群式供应链网络是以某一特定集群为平台形成的供应链网络系统,其中,包含2条或2条以上的单链式供应链网络,整个网络中至少存在2个核心企业,且核心企业之间同时包含合作与竞争关系,存在跨链合作。一方面,集群式供应链网络中既存在单链条水平方向上的生产活动,也存在跨链条的纵向协同合作关系,这意味着集群式供应链网络同时具备传统供应链网络和产业集群的特征。另一方面,集群式供应链网络由多个简单的供应链组成,其节点之间由于物流、信息流、资金流等关系而错综复杂的连接在一起,呈现出复杂网络的特点。
外部风险冲击会通过政策变化、市场波动等因素对企业要素利用成本造成影响,从而对企业本阶段产品的定价造成影响,进而影响企业向下一阶段企业提供产品的概率,最终对企业向下一阶段提供产品数量产生影响[14]。在一个集群式供应链网络中,整个网络是由多个简单的供应链网络组合而成,因此,集群式供应链网络表现出复杂网络特征,导致上一阶段的企业向该阶段企业提供产品的数量同样会受影响。集群式供应链网络呈现高度复杂性,节点数目多,各个节点之间的联系错综复杂。风险通过节点之间的业务往来,主要靠资金流、信息流、物流和人才技术等作为传播的载体,受到风险传播的节点会将风险传播给与其相邻的上下游节点,并且风险不能直接传播给没有直接业务联系的节点,但相邻的节点传播后会将风险再次传递与之相邻的节点,进而波及整个集群式供应链网络。
基于级联失效的风险传播机制如图1所示。其中,图1a中所有节点均处于正常运转状态,未失效。现假设存在一个外部风险对该网络的节点a产生冲击,若第一轮外部风险冲击节点a并导致节点a失效,当节点a失效后,其承载的负载会遵循一定的分配规则向其相邻节点b、节点c、节点d、节点e、节点f和节点g进行分配,级联失效传播过程如图1b所示。由图1可知:节点a失效后,其负载会按照图1b中的虚线箭头所指方向重新分配给节点a的所有邻居节点,在每一个邻居节点接收来自节点a的负载量之后,判断其新负载量是否在节点正常运行的范围之内,若在正常范围内,则未失效;反之,若节点的负载量超出或小于节点正常运行的范围,则判断该节点失效。节点a失效后,其负载量被重新分配给邻居节点后,节点b、节点c、节点e、节点f和节点g未失效,节点d因为节点a失效所产生的级联失效现象而失效。节点d失效后,其负载量会继续按照图1c的虚线箭头方向,按照上一轮相同的负载重分配规则将负载重新分配给与之相邻的节点k、节点l和节点m,再次判断该重新分配过程是否造成新一轮的节点失效,按照这个步骤继续循环,直到整个网络的节点全部失效或未出现新的失效节点后,风险传播结束,级联失效过程同样结束。
节点的初始负载 L r 0表示网络在初始状态下节点所能承受的负载量。理论上,一个节点与更多的节点存在联系,当遭到外部风险冲击后,其能够迅速恢复但相较之下也会遭受到更多的风险冲击。在集群式供应链网络中,不同节点的属性不同,多数企业同时兼有供应商和采购商2种角色,即同时包含供给量和需求量,且不同企业与外界的联系强度也存在差异。参照文献[15],将每一个节点均设定一个初始需求负载 L r D和初始供给负载 L r S L r D与节点的入度相关, L r S与节点的出度相关,即 L r 0包含 L r D L r S 具体表示如下:
L r 0 = k r l φ r I φ r O k l α r = 1,2 N
L r D = k r I l φ r I φ r O k l α r = 1,2 N
L r S = k r O l φ r I φ r O k l α r = 1,2 N
式中: k r为节点r的度; k r I为节点r的入度; k r O为节点r的出度; N为集群式供应链网络中的节点数; φ r I为节点r在集群式供应链网络中上游节点企业的节点集合; φ r O为节点r在集群式供应链网络中下游节点企业的节点集合; α为调控网络节点负载的参数,α的不同取值会得到不同的网络节点负载。
节点容量代表一个节点所能承受的负载范围,在现实中受制于成本等因素的影响,节点所能承受的容量不可能没有上限和下限,如电力系统中每个变电站存在一个最大的传输电力、企业为维持正常生产流程需要一定的库存量。节点容量上限越大,代表节点能够承受更大的风险,其抗风险能力更强。节点容量下限越小,表示为维持节点正常运转的容量要求更低,其抗风险能力越强。节点容量上限 L r m a x和节点容量下限 L r m i n用下式表示:
L r m a x = ( 1 + β ) L r S 0 < β < 1
L r m i n = γ L r D 0 < γ < 1
式中: β为企业的战略弹性参数; γ为企业的外部依赖参数。
当网络中某节点失效后,其所承担的负载量会以某种分配规则向其相邻节点进行负载重分配。参照文献[16],按照节点初始负载在所有相邻节点的初始负载之和中所占比重进行负载分配,如下式:
Δ L y r = L r L y n σ r L n
式中: Δ L y r为节点r瘫痪后,其邻居节点y被分配的负载量; L r为节点r的初始负载; σ r为全部与节点r相邻节点的集合。考虑到一个节点的需求和供给可能不会同时发生变化,因此,考虑2种情形,具体如下所示:
L e S ' = L e S + L r S ' L e S n σ r L n S
L y D ' = L y D ' - L r D ' L y D n σ r L n D
式中: L e S '为下游节点r失效后节点e负载重分配后新的供给负载,由于节点r是节点e的采购商,因此,当节点r失效后导致节点e的供给负载增加; L y D '为上游节点r失效后节点y负载重分配后新的需求负载,由于节点r是节点y的供应商,因此,当节点r失效后导致节点y的需求负载减少。
节点失效进行负载重分配后需要判断哪些节点为新一轮失效节点。进行负载重分配后节点新的负载如下所示:
L f t + 1 = L f t + Δ L f r t + 1
L f m i n L f t + 1 L f m a x 此时,节点f在其所能承受的负载范围之内,未失效。若 L f t + 1 < L f m i n L f t + 1 > L f m a x 则判断节点f此时为失效节点,造成新一轮级联失效。
L y t + 1 = L y t - Δ L a y t + 1
L y m i n L y t + 1 L y m a x 此时,节点y在其所能承受的负载范围之内,未失效。若 L y t + 1 < L y m i n L y t + 1 > L y m a x 则判断节点y此时为失效节点,造成新一轮级联失效。
为衡量外部风险冲击对集群式供应链网络的影响程度以及评价恢复策略的作用,选择受冲击后未失效节点数占初始网络节点总数的比值作为指标,并定义为网络效率E,该指标越大,说明外部风险对集群式供应链网络的冲击性越小,恢复策略越有价值。计算如下:
E = W ' ' W
式中: W ' '为在受到外部风险冲击后集群式供应链网络中未失效的节点数量; W为未受到外部风险冲击的集群式供应链网络初始节点数量。
在集群式供应链网络中,如果一个企业与更多的企业进行合作,一旦这个企业停止运行甚至破产,会殃及很多企业的业务,最终影响整个集群式供应链网络的正常运行。因此,这些重要企业抗风险的能力对整个集群式供应链网络的稳定性至关重要。重要企业实施风险耐受性提升策略能够有效提升重要企业抗风险能力,一定程度上阻挡外部风险在集群式供应链网络中的传播以及减少,因此,受影响的企业数量,能够有效规避外部风险对其造成的不利影响,达到有效维持整个集群式供应链网络正常运行的状态。在接下来的仿真中,通过Python生成200个节点的有向无权无标度网络进行试验。首先,将度中心性排名前20节点的容量边界扩大,其容量边界的参数设置为β=0.9,γ=0.1,其他节点的容量边界的相关参数设置为β=0.3,γ=0.7,在进行首次攻击时,假设10个节点失效。
重要企业实施风险耐受性提升策略前后网络效率对比如图2所示。由图2可知:在重要企业未实施风险耐受性提升策略之前,随机攻击下,风险在网络中传播4次后网络效率就已经降至0.295,风险在网络中传播6次后,节点全部失效,网络效率变为0;蓄意攻击下,风险在网络中传播3次后网络效率就降至0.300,风险在网络中传播6次后,节点全部失效,网络效率变为0。在重要企业实施风险耐受性提升策略后,随机攻击下,风险在网络中传播14次后风险传播停止,网络效率为0;蓄意攻击下,风险在网络中传播11次后风险传播停止,节点全部失效,网络瘫痪。虽然蓄意攻击和随机攻击下最终节点同样全部失效,网络瘫痪,但是重要企业实施风险耐受性提升策略,延缓了风险导致企业的大面积失效,在风险传播2次后,网络效率仍然高达0.765。因此,重要企业实施风险耐受性提升策略有利于企业在风险传播开始的前几次就及时找出失效节点进行补救,避免该失效节点造成下一轮失效,从而将风险阻挡在前几步,避免其影响其他节点。
通过对重要企业实施风险耐受性提升策略使其承担风险的能力更强,一定程度上能够减少每一次风险传播所引起的失效节点数,延缓了集群式供应链网络的瘫痪速度,但无法避免整个集群式供应链网络最终瘫痪。进一步,在现实中如果对重要企业实施风险遏止能力提升策略,能够最大程度地避免重要企业遭受外部风险的冲击,同时,规避重要企业失效后对集群式供应链网络中其他企业造成的不利影响,减少风险在集群式供应链网络中的扩散和放大效应,遏止风险在集群式供应链网络中传播,避免集群式供应链网络瘫痪。该节依然通过Python生成200个节点的有向无权无标度网络进行试验。所有节点的容量边界保持不变,相关参数依然设置为β=0.3,γ=0.7,在进行策略仿真过程中,假设度中心性排名前5的节点一直不会失效,永远处于正常运转状态,在进行首次攻击时,假设20个节点失效。由于在该策略仿真过程中假设度中心性排名前5的节点不失效,因此,此处不考虑蓄意攻击,仅考虑随机攻击。
重要企业实施风险遏止能力提升策略前后网络效率对比如图3所示。由图3可知:在重要企业未实施风险遏止能力提升策略之前,随机攻击下,风险在网络中传播4次后网络效率就降至0.180,风险在网络中传播6次后,节点全部失效,网络效率变为0,在重要企业实施风险遏止能力提升策略后,风险仅在网络中传播3次就停止,网络效率高达0.870,网络仍然保持高效运转。因此,重要企业实施风险遏止能力提升策略,可有效减少风险传播造成的失效节点数,还可阻止风险造成节点全部失效和网络瘫痪,助力网络维持高效运转,相较于重要企业实施风险耐受性提升策略,能更有效地遏制外部风险冲击下集群式供应链网络风险传播,防止集群式供应链网络瘫痪。
扩大集群式供应链网络中所有节点的容量边界可提升各个节点在面对外部风险时的应对能力。供应链全局实施风险耐受性提升策略,能够使整个集群式供应链网络韧性得到提升,减少企业外部依懒性,提升整个集群式供应链网络应对外部风险的能力,有利于企业在遭受外部风险冲击后能够尽快恢复到正常运行状态。通过Python生成200个节点的有向无权无标度网络进行仿真试验,供应链全局未实施风险耐受性提升策略前所有节点的参数为β=0.3,γ=0.7。扩大所有节点的容量边界,将所有节点容量边界相应参数设置为β=0.8,γ=0.2,在进行首次攻击时,假设有20个节点失效。
供应链全局实施风险耐受性提升策略前后网络效率对比如图4所示。由图4可知:在供应链全局未实施风险耐受性提升策略之前,随机攻击下,风险在网络中传播4次后网络效率就已降至0.2,风险在网络中传播6次后,节点全部失效,网络效率变为0;蓄意攻击下,风险在网络中传播3次后,网络效率就降至0.075,风险在网络中传播5次后,节点全部失效,网络效率变为0。在供应链全局实施风险耐受性提升策略后,随机攻击和蓄意攻击均未造成节点全部失效,网络未瘫痪。随机攻击下,风险在网络中传播17次后停止传播,未造成下一轮节点的失效,此时网络效率为0.125,蓄意攻击下,风险在网络中传播14次后风险传播停止,未造成下一轮节点的失效,此时网络效率为0.210。因此,供应链全局实施风险耐受性提升策略,不仅减少风险造成的失效节点数,还能防止风险造成节点全部失效和网络瘫痪。重要企业实施风险耐受性提升策略仅能延缓集群式供应链网络瘫痪,并不能彻底阻止风险在集群式供应链网络中传播和集群式供应链网络的瘫痪,相较于仅对重要企业实施风险耐受性提升策略,该策略能够更有效地防止更多企业失效和集群式网络瘫痪。
现实中,通过建立供应链风险分担机制,当集群式供应链网络中部分企业面对风险冲击失效后,政府、市场和企业共同承担该风险,能够减轻企业压力,有效规避外部风险冲击对企业造成的不利影响,并降低其他企业失效和集群式供应链网络瘫痪的可能性。依然通过Python生成200个节点的有向无权无标度网络进行仿真试验。在策略仿真过程中,节点的容量边界保持不变,相关参数设置为β=0.3,γ=0.7,在进行首次攻击时,假设20个节点失效。负载重分配过程中,假设邻居节点仅承受来自失效节点60%的负载,在建立供应链风险分担机制策略仿真过程中,使用下式进行负载重分配:
Δ L y r = 0.6 L r L y n σ r L n
建立供应链风险分担机制前后网络效率对比如图5所示。从图5可以看出,在未建立供应链风险分担机制前,随机攻击下,风险在网络中传播4次后网络效率降至0.180,风险在网络中传播6次后,节点全部失效,网络效率变为0;蓄意攻击下,风险在网络中传播3次后网络效率降至0.080,风险在网络中传播5次后,节点全部失效,网络效率变为0。在建立供应链风险分担机制后,随机攻击下,风险在网络中传播9次后风险传播停止,未造成下一轮节点的失效,此时网络效率为0.225;蓄意攻击下,风险在网络中传播6次后风险传播停止,网络效率为0。因此,通过建立供应链风险分担机制,虽然不能阻止蓄意攻击下网络瘫痪,但能有效减少随机攻击下风险造成的失效节点数,还可防止风险造成节点全部失效和网络瘫痪。
1) 重要企业实施风险耐受性提升策略不能阻止网络最终崩溃,但可有效延缓风险传播的速度并减轻每一步风险传播过程中的影响程度。
2) 重要企业实施风险遏止能力提升策略,可有效减少风险传播造成的失效节点数,还可阻止风险造成节点全部失效和网络瘫痪,助力网络维持高效运转,相较于重要企业实施风险耐受性提升策略,能更有效地遏制外部风险冲击下集群式供应链网络风险传播,防止集群式供应链网络瘫痪。
3) 供应链全局实施风险耐受性提升策略可有效阻止风险在网络中的蔓延并防止网络的崩溃,相较于仅对重要企业实施风险耐受性提升策略,该策略更有效。
4) 建立供应链风险分担机制不能阻止蓄意攻击下集群式供应链网络瘫痪,能有效减少随机攻击下失效节点数并防止集群式供应链网络瘫痪。
  • 湖北省自然科学基金资助(2022CFB751)
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2025年第35卷第4期
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doi: 10.16265/j.cnki.issn1003-3033.2025.04.1080
  • 接收时间:2024-11-25
  • 首发时间:2025-07-05
  • 出版时间:2025-04-28
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  • 收稿日期:2024-11-25
  • 修回日期:2025-02-18
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湖北省自然科学基金资助(2022CFB751)
作者信息
    1 中国地质大学(武汉) 经济管理学院,湖北 武汉 430078
    2 三峡大学 经济与管理学院,湖北 宜昌 443002
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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