Supply chain

Risk analysis (engineering)

Mathematical optimization

Engineering

Control engineering

Business

Marketing

Supply chain risk management

Robust optimization

Transshipment (information security)

Supply chain network

Closed loop

Mathematics

Computer science

Operations research

Supply chain management

Service management

Lagrangian relaxation

In today’s globalized and highly uncertain business environments, supply chains have become more vulnerable to disruptions. This paper presents a stochastic robust optimization model for the design of a closed-loop supply chain network that performs resiliently in the face of disruptions. The proposed model is capable of considering lateral transshipment as a reactive strategy to cope with operational and disruption risks. The objective is to determine facility location decisions and lateral transshipment quantities that minimize the total supply chain cost across different disruption scenarios. A Lagrangian relaxation algorithm is developed to solve the robust model efficiently. Important managerial insights are obtained from the model implementation in a case study of glass the industry.

Closed-loop supply chain network design under disruption risks: A robust approach with real world application