Seyyed-Mahdi Hosseini-Motlagh, Sara Cheraghi, Mohammadreza Ghatreh Samani,
Volume 27, Issue 4 (IJIEPR 2016)
Abstract
The eternal need for humans' blood as a critical commodity makes the healthcare systems attempt to provide efficient blood supply chains (BSCs) by which the requirements are satisfied at the maximum level. To have an efficient supply of blood, an appropriate planning for blood supply chain is a challenge which requires more attention. In this paper, we address a mixed integer linear programming model for blood supply chain network design (BSCND) with the need for making both strategic and tactical decisions throughout a multiple planning periods. A robust programming approach is devised to deal with inherent randomness in parameters data of the model. To illustrate the usefulness of the model as well as its solution approach, it is tested into a set of numerical examples, and the sensitivity analyses are conducted. Finally, we employ two criteria: the mean and standard deviation of constraint violations under a number of random realizations to evaluate the performance of both the proposed robust and deterministic models. The results imply the domination of robust approach over the deterministic one.
Mohammad Reza Ghatreh Samani, Jafar Gheidar-Kheljani,
Volume 34, Issue 3 (IJIEPR 2023)
Abstract
In this paper, a brief review of the recently developed blood supply chain (BSC) management studies is firstly presented. Then, a first-ever multi-objective robust BSC model is proposed, which is inspired by the need for an integrated approach towards improving the performance of BSC networks under uncertain conditions. The network efficiency by minimizing cost, adequacy by providing reliable and sufficient blood supply, and effectiveness by controlling blood freshness are aimed at the proposed model. A two-phase approach based on robust programming and an augmented epsilon-constraint method is devised to model the uncertainty in parameters and provides a single-objective counterpart of the original multi-objective robust model. We investigate a case to illustrate the real-world applicability of the problem. The research comes to an end by performing some sensitivity analyses on critical parameters, and the results imply the capability of the model and its solution technique.
