A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics

Abstract

Crisis management is one of the significant challenges when a disaster occurs. Post disaster operations mainly focus on medical treatments. One of the general requirements of medical treatments of field hospitals in disaster is blood transfusion. This way, configuring a blood transfusion network to handle the appropriate assignments and distribution is proved to influence the type, number and severity of recent crises. Therefore, obtaining effective solutions, containing determining the optimal location of facilities of field hospitals, identifying the optimal capacity of facilities and transportation, blood transfusion routes to reduce costs and injustices in this area in times of crisis is essential. Hence, in this study, we proposed a model, intend for allocating blood types and regarding the lifetime of blood to tackle with field hospitals in the disaster sites. Furthermore, the coverage of the demand for blood types for the establishment of field hospitals and the balanced distribution of blood between these hospitals from the blood collection level to the establishment of field hospitals with optimal routing of blood transfusion at the lowest cost is considered. The proposed model for the crisis response management phase is modelled applying a multi-objective mathematical model under uncertain conditions. To cope with the uncertain data arisen in the disaster condition, the robust optimization approach is applied. Then, to solve the proposed model, a hybrid method based on Genetic Algorithm and Multi-Choice Goal Programming (GAMCGP) under uncertainty is developed. In addition, the proposed model is successfully applied to a real- world blood supply chain in Mazandaran, northern part of Iran, and the results validate the performance of the proposed model. It also proves that the suggested model can be considered as a new approach for blood supply chain tactical management in the crisis logistics.

Keywords:

• Robust optimization
• location and routing
• blood supply chain
• lifetime of blood
• Genetic Algorithm Multi-Choice Goal Programming (GAMCGP)

Disclosure statement

There is no conflict of interest for this paper.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.