In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed.

During the past few years, operations research applications in health care operation management have grown quickly. On the other hand blood as a perishable, valuable and lifesaving product is one important asset of any healthcare center. Therefore, designing a blood supply network comes to importance. It also should be noted that a blood supply chain comprises specific modifications. This study intends to locate blood bank components in a network, and to determine the allocations among the network components. The supply chain components considered in this study are donation sites, testing and processing labs, blood banks, and demand points. It is known that demand centers such as hospitals and clinics highly depend on blood products and any deficiency in procurement can even result in a person’s death. Thus, in the last layer of the considered network a transshipment sub-network is considered between demand points. Most of the intricacies in problem formulation of blood supply chain are regarded in this study; cases such as blood wastage, blood product decomposition in lab facilities, and transshipments between demand points. Due to the fact that for such an important and lifesaving supply chain the aim would go beyond minimizing cost, another objective function is presented for the problem. Hence, to obtain a Pareto solution for both objective functions ?-constraint method is utilized. Finally, to demonstrate the applicability of the problem, the model is implemented on a number of problem sets.

The blood supply chain (BSC) is a crucial and intricate system in the healthcare sector, which is marked by perishable products, fluctuating supply and demand, and a major impact of inefficiency. This paper showcases a detailed scientific review of BSC literature from 2010 to 2025 through scientometric methods, thereby mapping out its intellectual structure and development. By scrutinizing both foundational and recent publications, the authors are able to point out the research streams, methodological trends and main scholars. The scrutiny brings forward three leading research paradigms: (1) robust and resilient network design for disaster response, with Jawad as the leading scholar; (2) green and sustainable BSC modeling under uncertainty, where Pishvaee and his team are the main contributors; and (3) integrated inventory-routing problems for perishables, with Ramezanian as the pivotal author. This discipline is moving away from deterministic, single-objective models to the development of intricate multi-objective frameworks under hybrid uncertainties (robust, fuzzy, stochastic) which are being solved increasingly with metaheuristics and supported by case studies from real applications. The new trends include the combination of AI/ML for forecasting and decision-making, blockchain for transparency, and drones for the delivery part. The present review collects all these advancements and gives a succinct direction for both researchers and practitioners.