Incorporating delay propagation into decision-making for optimizing prefabricated building supply chain networks under uncertainty

Abstract

Prefabricated buildings are crucial for the transformation of the construction industry, while the Prefabricated Building Supply Chain Network (PBSCN) that supports their implementation is subject to uncertainties in production, transportation, and installation. These uncertainties lead to schedule delays and cost increases, which significantly hinder the widespread adoption of prefabricated buildings. To address these issues, this paper develops a three-tier optimization model that integrates component factories, logistics providers, and contractors to improve resource allocation and reduce total costs. This model explicitly accounts for uncertainty-induced delay propagation across stages and incorporates its impacts into the decision-making process through work stoppage cost at the construction site. A Scenario-Based Stochastic Programming (SBSP) approach is employed to determine optimal decisions, while Monte Carlo Simulation (MCS) is utilized to generate representative scenarios. Furthermore, the proposed model is extended to incorporate a carbon trading mechanism to examine the interaction between environmental regulation and supply chain decisions. The model’s effectiveness is validated through a hypothetical case adapted from a real-world project, in which the optimal solutions involved concentrating approximately 6% of orders in the baseline case and 33.0%–35.5% in the large-scale experiment. Results show that proactively accounting for uncertainties not only reduced costs but also strengthened coordination among entities to improve resource utilization. This paper provides practical decision support for PBSCN stakeholders, helping them mitigate risks, optimize order allocation, and improve overall supply chain performance in an uncertain environment.

Graphical abstract

Keywords

prefabricated buildings / supply chain network coordination / uncertainty management / scenario-based stochastic programming / cost optimization

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