Robustness of interdependent supply chain networks against both functional and structural cascading failures
Robustness of interdependent supply chain networks against both functional and structural cascading failures
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DOI：
10.1016/j.physa.2021.126518
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发表时间：
2021-10-22
影响因子：
3.3
通讯作者：
Deng, Dingshan
中科院分区：
文献类型：
--
作者：
Shi, Xiaoqiu;Long, Wei;Deng, Dingshan
供应链系统可以看作是一个由无向信息网络和有向物理网络组成的相互依赖的供应链网络。为了抵御中断，ISCN需要保持运营和连通性，即鲁棒性。考虑功能和结构级联故障时ISCN的鲁棒性的研究仍然很少。在本文中，我们首先提出了一个级联故障模型，同时考虑这两个级联故障。我们还提出了一个模型，以产生不同的网络类型和互连模式的ISCN。基于所提出的全连通子网络的转移阈值，可以更准确地评估ISCN的鲁棒性。然后，我们进行数值模拟，以研究如何一些参数（例如，网络类型、互连模式、不同类型节点的分布等）。影响ISCN在随机和有针对性的中断下的鲁棒性。研究结果表明，不同的网络类型、互连模式和中断类型对ISCN的鲁棒性影响较大;不同类型的节点分布越均匀，对应的ISCN的鲁棒性越好，无论是哪种中断类型。我们的研究结果可以为构建健壮的ISCN提供帮助。(C)2021爱思唯尔有限公司版权所有。
A supply chain system can be considered as an interdependent supply chain network (ISCN) which consists of an undirected cyber-network and a directed physical-network. To survive against disruptions, an ISCN needs to maintain operations and connectedness, referred to as robustness. Studies on the robustness of ISCNs when considering both functional and structural cascading failures are still scarce. In this paper, we first propose a cascading failure model which considers these two cascading failures simultaneously. We also present a model to generate ISCNs with different network types and interconnecting patterns. Using the transition threshold based on the proposed all-type connected sub-network, we can evaluate the robustness of ISCNs more properly. We then conduct numerical simulations to investigate how some parameters (e.g., network type, interconnecting pattern, the distribution of different types of nodes, etc.) affect the robustness of ISCNs under random and targeted disruptions. The results mainly show that the robustness of ISCNs can be affected seriously by different network types, interconnecting patterns, and disruption types; and the distribution of different types of nodes is more uniform, the corresponding ISCN is more robust, no matter what the disruption type is. Our results may provide help for building robust ISCNs. (C) 2021 Elsevier B.V. All rights reserved.