A two-stage stochastic collaborative intertwined supply network design problem under multiple disruptions

An intertwined supply network (ISN) is a set of interconnected supply chains with dynamic structures that efficiently provide products and services to numerous customers. ISNs inherently involve collaboration among several enterprises that have their own supply chain. Such collaboration is pivotal in their long-term resiliency under major disruptions. This paper studies a two-stage stochastic network design problem that incorporates three different resilience strategies to design ISNs under a fair collaboration. We consider capacity expansion, capacity sharing, and rerouting as resilience strategies. We consider disruptions to be stochastic and to incorporate two sources of uncertainty: transportation costs and available capacity at facilities. We develop a Monte-Carlo simulation-based algorithm that uses the sample average approximation scheme to generate high quality solutions for our problem. We analyze the robustness and efficiency of the proposed model on a set of instances with up to 117 nodes and six collaborating enterprises. The value of collaboration on the ISN performance is assessed by a thorough sensitivity analysis of different metrics. In particular, we evaluate the impact of different levels and scales of random disruptions and flexibility on the network performance, which provides insights into the balance between cost and resilience.