Robustness of prefabricated construction supply chain network against underload cascading failure

¹ Center for Green Engineering and sustainable Development, Chang’an University, Xi’an, Shaanxi 710064, P.R. China
² School of Economics and Management, Chang’an University, Xi’an, Shaanxi 710064, P.R. China
³ Xi’an Key Laboratory of Green Infrastructure Construction and Operation, Xi’an, Shaanxi 710064, China

^* Corresponding author: 2021123060@chd.edu.cn

Received: 17 November 2023
Accepted: 8 June 2024

Abstract

A prefabricated construction supply chain(PCSC) is a complex network with high interdependency between entities. After disturbance, it is prone to cascading failure, leading to project delays or budget overruns. Therefore, it is necessary to model a robust network against cascading failure to achieve a sustainable prefabricated construction system. This study explores the functional robustness of a prefabricated construction supply chain network (PCSCN) against underload cascading failure. First, the PCSCN is constructed as a three-echelon supply chain network based on complex network theory, which can characterize the general characteristics of PCSC and provide the network foundation for the subsequent numerical simulation research. Then, a more realistic underload cascading failure model that adds the new element of substitute nodes is established to describe load loss propagation in the PCSCN. Finally, the Order Fulfillment Rate(OFR) is used as the robustness index to quantify network robustness from a functional perspective. The numerical simulation results indicate that in the PCSCN, the larger the initial load is, the more important the node, and component manufacturers are more important than building material suppliers. In addition, the node capacity upper bound parameter α has a positive relationship with robustness, the failure coefficient β has a negative relationship, and the edge weight adjustment coefficient θ has no significant impact on robustness. This research can provide guidance for developing cascade control and defense strategies in PCSCN risk management.