A two-stage fuzzy optimization model for scarce drugs supply and ration planning under uncertainty: A case study

The scarce drugs rationing is a serious challenge in the public health system. This study proposes a two-stage linear programming model for the scarce drug rationing problem which reflects the conflicts of interest between supply chain members. The proposed model uses a rolling horizon planning mechanism along with an iterative procedure for supplying and rationing the scarce drugs. The maximization of minimum suppliers’ profit and the minimization of maximum shortage are considered as the first and second stage models’ objective functions, respectively. Furthermore, two need-based indicators including disability-adjusted life years (DALYs) and population are utilized to enhance the performance of the proposed model. Besides, human capital method is applied for calculating the shortage costs of demand zones. Due to the inherent epistemic uncertainty in some critical input parameters such as the exchange rate and demand, a credibility-based robust possibilistic programming approach is used to deal with imprecise parameters. Finally, the applicability and usefulness of the proposed approach are investigated by a real case study from Iranian food and drug administration. The computational results demonstrate the superiority of robust programming model against deterministic one.