Design of distributionally robust closed-loop supply chain network based on data-driven under disruption risks

Abstract

In globalised and highly uncertain business environment, it is necessary to design a supply chain that is not only efficient but also resilient, with continuity to operate and meet demand in the face of disruption. Aiming at this problem, a two-stage distributionally robust optimisation model based on data-driven is established to design a closed-loop supply chain network that can be flexibly executed in case of disruption. To analyse the resilience of the supply chain, the model considers the possible random disruptions of two types of facilities, and aims to deal with them through active and passive strategies such as supplier fortifying, recovery, signing with backup suppliers, and lateral transshipment. In addition, in view of the uncertainty of disruption scenarios and the limited disruption historical data available, distributionally robust optimisation method with the Wasserstein ambiguity set is used. In solving, the established robust model is transformed into a tractable model form using duality and linearisation technology, and solved by Gurobi solver. The numerical results show: Considering resilient measures effectively mitigate disruption hazards; Adding the recycling strategy can significantly reduce the production costs; Comparing with stochastic and classical robust optimisation models, the performance of the model established in this paper is highlighted.

Keywords:

• Closed-loop supply chain network
• distributionally robust
• risk and uncertainty
• two stage programming
• supply chain resilience

Acknowledgments

The authors thank the editor-in-chief and the anonymous reviewers for their valuable comments, which helped us to make great improvements to the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, [K. S.], upon reasonable request.

Additional information

Funding

This work was supported by the Natural Science Foundation of Hebei Province (No. A2022201002); and the Post-graduate's Innovation Fund Project of Hebei Province (No. CXZZSS2023008).