The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach

• Published in: Transportation research. Part E, Logistics and transportation review Vol. 152; p. 102387
• Main Authors: Zhu, Xiaoyan, Cao, Yunzhi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2021
• Subjects: Location-inventory-transportation model; Recovery-fund based mitigation strategy; Risk-averse two-stage stochastic programming; Supply chain disruption management; Trust-region-based decomposition method; Supply chain disruption management; Trust-region-based decomposition method; Recovery-fund based mitigation strategy; Risk-averse two-stage stochastic programming; Location-inventory-transportation model
• ISSN: 1366-5545; 1878-5794
• DOI: 10.1016/j.tre.2021.102387

•Jointly optimized the supply chain location, inventory and recovery management.•Addressed uncertain disruptions on plant production capacities.•Proposed a recovery investment strategy to adjust the recovery speed and duration.•Developed a risk-averse two-stage stochastic model and a decomposition method.•Provided implications on supply chain design and operations and recovery investments. For a supply chain subject to uncertain production disruptions, the joint optimization of investment intervention on recovery speed and duration of disrupted production capacity and location and inventory management has not been well studied. In this paper, a novel recovery strategy is introduced and studied, which uses investment to adjust the recovery speed and duration of production capacity, and two recovery behaviors responding to different types of disruptions are modeled. Considering uncertain disruption scenarios and their ripple effects over the supply chain, a risk-averse two-stage stochastic programming model (RTSPM) is established to study the integrated supply chain management of selection of distribution centers, multi-period inventory, transportation flows, and recovery-fund based mitigation policy. The RTSPM incorporates the risk preference of managers in decision making. We propose a trust-region-based decomposition method to solve the RTSPM and demonstrate its efficiency by benchmarking on state-of-the-art commercial solvers. Through numerical examples, we deeply analyze the effectiveness of RTSPM and the relations of optimal recovery investment decisions with the uncertain disruption factors. Finally, we provide implications and suggestions induced from the models and findings to aid the decisions on renting of distribution centers and the emergency investment and operational decisions when suffering the disruptions.