TY - GEN
T1 - Identification of Key Node Sets in Tunneling Boring Machine Cutterhead Supply Chain Network Based on Deep Reinforcement Learning
AU - Li, Yinqian
AU - Wen, Jingqian
AU - Zhang, Yanzi
AU - Zhang, Lixiang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The supply chain is a network structure prone to disruptions due to its complexity. Specifically, tunnel boring machines (TBMs) are extensive and intricate equipment that undergo design, production, and construction simultaneously, further exacerbating the risks in the TBM cutterhead supply chain (TBMCSC). When a problem arises in an enterprise within the TBMCSC, the risk propagates along the supply chain, impacting other enterprises in the network. Although predicting risks in advance is deemed impossible, identifying the most vulnerable enterprises, which are referred to as key node sets, enables improved risk management. In light of this, this paper proposes a deep reinforcement learning (DRL)-based method for identifying key node sets in a TBMCSC. The approach involves the following steps: first, the entire TBMCSC is modeled using complex network theory (Step 1). Next, risk propagation processes on the network are revealed using the coupled map lattice (CML) method (Step 2). Finally, the DRL algorithm is used to identify key node sets in the TBMCSC, with the aim of maximizing the impact of risk propagation (Step 3). By comparing the extent of risk propagation of the critical node sets identified by the DRL method with the traditional methods when facing the same risks, the superiority of this approach is demonstrated.
AB - The supply chain is a network structure prone to disruptions due to its complexity. Specifically, tunnel boring machines (TBMs) are extensive and intricate equipment that undergo design, production, and construction simultaneously, further exacerbating the risks in the TBM cutterhead supply chain (TBMCSC). When a problem arises in an enterprise within the TBMCSC, the risk propagates along the supply chain, impacting other enterprises in the network. Although predicting risks in advance is deemed impossible, identifying the most vulnerable enterprises, which are referred to as key node sets, enables improved risk management. In light of this, this paper proposes a deep reinforcement learning (DRL)-based method for identifying key node sets in a TBMCSC. The approach involves the following steps: first, the entire TBMCSC is modeled using complex network theory (Step 1). Next, risk propagation processes on the network are revealed using the coupled map lattice (CML) method (Step 2). Finally, the DRL algorithm is used to identify key node sets in the TBMCSC, with the aim of maximizing the impact of risk propagation (Step 3). By comparing the extent of risk propagation of the critical node sets identified by the DRL method with the traditional methods when facing the same risks, the superiority of this approach is demonstrated.
KW - Complex network
KW - Deep reinforcement learning
KW - Key node sets identification
KW - Tunnel boring machine cutterhead
UR - http://www.scopus.com/inward/record.url?scp=85193297690&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-0194-0_71
DO - 10.1007/978-981-97-0194-0_71
M3 - Conference contribution
AN - SCOPUS:85193297690
SN - 9789819701933
T3 - Lecture Notes in Mechanical Engineering
SP - 737
EP - 748
BT - Proceedings of Industrial Engineering and Management - International Conference on Smart Manufacturing, Industrial and Logistics Engineering and Asian Conference of Management Science and Applications
A2 - Chien, Chen-Fu
A2 - Dou, Runliang
A2 - Luo, Li
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2023 and the 7th Asian Conference of Management Science and Applications, ACMSA 2023
Y2 - 17 November 2023 through 19 November 2023
ER -