Designing a responsive-sustainable-resilient blood supply chain network considering congestion by linear regression method

Blood Components (BCs) are highly perishable. Perishability, the short lifespan of BCs, and unpredictability in demand volumes make managing the Blood Supply Chain (BSC) more complex. Therefore, it is essential to expand a proper blood network model to tackle uncertainty and minimize the time of delivering blood to patients. This study presents an innovative multi-objective multi-echelon multi-period mixed-integer non-linear model for efficient, responsive, and Green Blood Supply Chain (GBSC) networks considering resilience measures that are divided into three assessment metrics and congestion in Permanent Blood Centers (PBCs). The suggested model aims at significantly reducing prices, Waiting Time (WT) in the system, and environmental damages while simultaneously boosting the supply chain networks' level of resilience. Apart from that, a linear regression is proposed to forecast the demand for blood to decrease the possibility of a shortage of BCs supply in the Blood Supply Chain Network (BSCN). To meet the suggested model, firstly, LP-metric method is used for solving small-sized problem instances. However, this method was inefficient for solving large-sized instances. So, the Lagrangian Relaxation (LR) method is employed. Additionally, the presented decision model's efficacy is evaluated against a case study from real life. Moreover, a sensitivity analysis is undertaken on important problem parameters to offer insightful managerial information.