Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption

This paper studies joint robust network design and recovery investment management in a production supply chain, considering limited historical data about disruptions and their possibilities. The supply chain is subject to uncertain disruptions that reduce production capacity at plants, and the cascading failures propagate along the supply chain network. A data-driven two-stage distributionally robust optimization model with Wasserstein ambiguity set (TWDRO) is constructed to determine the strategic location and tactical allocation decisions in the first stage as well as the operational production and inventory decisions and recovery policy by recourse in the second stage. This paper also proposes a model for depicting a recovery-fund based mitigation strategy, and the model is general in depicting the accelerated, constant-speed, and decelerated recovery processes. In addition, partial backorder policy is adopted to depict the customers’ choices upon product stockout. The TWDRO model is solved by converting it to a mixed integer linear programming model and designing a joint solution method using benders decomposition and genetic algorithm. The performance of TWDRO solutions is demonstrated through numerical experiments and a case study, benchmarking on the stochastic programming and robust optimization approaches. This paper shows the effectiveness and robustness of TWDRO and provides managerial implications and suggestions for supply chain disruption and recovery management.