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Multi-objective two-stage emergent blood transshipment-allocation in COVID-19 epidemic.

Yufeng ZhouJiahao ChengChangzhi WuKok Lay Teo
Published in: Complex & intelligent systems (2023)
The problem of blood transshipment and allocation in the context of the COVID-19 epidemic has many new characteristics, such as two-stage, trans-regional, and multi-modal transportation. Considering these new characteristics, we propose a novel multi-objective optimization model for the two-stage emergent blood transshipment-allocation. The objectives considered are to optimize the quality of transshipped blood, the satisfaction of blood demand, and the overall cost including shortage penalty. An improved integer encoded hybrid multi-objective whale optimization algorithm (MOWOA) with greedy rules is then designed to solve the model. Numerical experiments demonstrate that our two-stage model is superior to one-stage optimization methods on all objectives. The degree of improvement ranges from 0.69 to 66.26%.
Keyphrases
  • coronavirus disease
  • sars cov
  • machine learning