Abstract
We present a stochastic optimisation model that can be used to design a resilient supply chain operating under random disruptions. The model aims to determine sourcing and network design decisions that minimise the expected total cost while ensuring that the minimum customer service level is achieved. The proposed model incorporates several resilience strategies including multiple sourcing, multiple transport routes, considering backup suppliers, adding extra production capacities, as well as lateral transshipment and direct shipment. A multi-cut L-shaped solution approach is developed to solve the proposed model. Data from a real case problem in the paint industry is utilised to test the model and solution approach. Important managerial insights are obtained from the case study. Our analyses focus on (1) exploring the relationship between supply chain cost and customer service level, (2) examining the impacts of different types of disruptions on the total cost, (3) evaluating the utility of resilience strategies, (4) investigating the benefits of the proposed solution approach to solve problems of different sizes and (5) benchmarking the performance of the proposed stochastic programming approach.
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