Journal
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
Volume 134, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.tre.2020.101840
Keywords
Blood supply network; Disaster relief; Stochastic distributionally robust optimization; Transshipment; Semidefinite programming
Categories
Funding
- National Natural Science Foundation of China [71971053, 71832001]
- MOE (Ministry of Education in China) Project of Humanities and Social Sciences [18YJA630129]
- Shanghai Philosophy and Social Science Program [2019BGL036]
- Fundamental Research Funds for the Central Universities
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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.
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