Highlights A stochastic blood supply network optimization considering disasters is studied. A novel distributionally robust model with integer recourse is formulated. A tractable approximate reformulation of min-max-min problems is proposed. A case study for the Longmenshan Fault in China is performed. Findings and managerial implications related to this problem are provided.
Abstract We study blood supply network optimization considering disasters where only a small number of historical observations exist. A two-stage distributionally robust optimization (DRO) model is proposed, in which uncertain distributions of blood demand are described by a moment-based ambiguous set, to optimize blood inventory prepositioning and relief activities together. To solve this intractable DRO with integer recourse, an approximate way is developed to transform it into a semidefinite program. A case study, based on the Longmenshan Fault in China, validates that our approach outperforms typical benchmarks, including deterministic, stochastic and robust programming. Sensitivity analysis provides helpful managerial insights.
A distributionally robust optimization for blood supply network considering disasters
2020-01-04
Article (Journal)
Electronic Resource
English