Proactive transshipment in the blood supply chain: A stochastic programming approach

Abstract

The main objective when managing inventories in blood supply chains is to establish an efficient balance between the wastage and shortage of blood units. The uncertain demand and the perishable nature of blood units can result in over-or under-stocking and increase wastage and shortage costs. In this study, we analyze how a proactive transshipment policy can avoid future shortages in addition to mitigate wastage. We consider a network of hospitals with uncertain demand in which each hospital makes decisions on the quantity to order from a central blood bank and to transship to other hospitals in each review period. We formulate the problem as a two-stage stochastic programming model. To generate scenarios, the Quasi-Monte Carlo sampling approach is employed and the optimal number of scenarios is determined by conducting stability tests. We performed numerical experiments to evaluate the performance of the proposed model and investigate its potential benefits of the outlined proactive transshipment. The developed model is used to compare the optimized policy with the current practice in some hospitals in Australia and with a no-transshipment policy. The numerical results indicate significant potential cost savings in comparison with the current policy in use and the no-transshipment policy.

A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak

“…Dehghani et al. [33] proposed a stochastic mathematical model for blood supply chain management. Considering proactive transshipment and order quantities simultaneously was one of the key novelties in their research.…”

“…Compared with the research carried out by Dehghani et al. [33] , our research took the advantage to investigate the transportation capacity and substitution priority accompanied with a real case study. Besides, locating collection centers and allocating centers to hospitals are considered of other study advantages.…”

A blood supply chain network design with interconnected and motivational strategies: a case study

“…Due to the above categories interdependency, some of the most recent researches attempted to tackle this problem more comprehensively by integrating them (Hosseini-Motlagh et al 2020a;Karadağ et al 2021;Dehghani et al (2021).…”

“…Different facilities in the same echelon can share their blood storage using this feature to decrease blood shortage while using the same limited stock of blood units. Some researchers have recently implemented this policy successfully (Dehghani et al 2021;Ghatreh Samani and Hosseini-Motlagh 2021). However, the mentioned studies only considered this policy in hospitals and did not extend it to blood centers, which sounds necessary, especially during a disaster.…”

Blood supply chain: a two-stage approach for tactical and operational planning

“…The third group of constraints ( 9)-( 14) models the flow and the inventory of bloodderived products, and are mostly based on the formulation described by Dillon et al (2017). An interesting study could be to use a more recent way of modelling inventory proposed by Dehghani et al (2019), which showed potential to reduce computational times. Constraints ( 9) and ( 10) are flow conservation constraints for bloodderived products and, essentially, they ensure that products produced/purchased in a given period which are not transported, in case of constraints (9), or consumed, in case of constraints (10), are kept as inventory at processing centres or demand zones, respectively.…”