Blood Supply Chain Network Design Research Articles

A two‐stage adaptive robust model for designing a reliable blood supply chain network with disruption considerations in disaster situations

AbstractWe consider multi‐period blood supply chain network design in disaster situations that involve blood donor groups, permanent and temporary blood collection facilities, blood banks, and hospitals. We use a discrete scenario set to model the uncertain blood supply and demand, and the unforeseeable disruptions in permanent blood collection facilities, blood banks, and road links arising from a disaster, where instead of complete failure, disrupted permanent blood collection facilities and blood blanks may only lose part of their capacities. To design a reliable blood supply network to mitigate the possible disruptions, we present a two‐stage adaptive robust model that integrates the location, inventory, and allocation decisions incorporating a blood sharing strategy, where blood can be delivered from a disrupted/non‐disrupted blood bank to disrupted blood banks to enhance the flexibility of the relief network. For this novel problem, we devise an exact algorithm that integrates column‐and‐constraint generation and Benders decomposition and introduce several non‐trivial acceleration techniques to speed up the solution generation process. We conduct extensive numerical studies on random data sets to evaluate the algorithmic performance. We also conduct a case study in Tehran to demonstrate its real‐life applicability and examine the impacts of key model parameters on the solutions. The numerical results verify the benefits of our model over typical benchmarks, that is, deterministic and stochastic models, and the superiority of our solution algorithm over the CPLEX solver and two well‐known solution approaches, that is, column‐and‐constraint generation and Benders decomposition. Finally, based on the numerical results, we derive managerial insights from the analytical findings.

Presenting a Bi- objective Integrated Model of Blood Supply Chain Network Design with the Approach of Increasing the Quality of Products

Presenting a Bi- objective Integrated Model of Blood Supply Chain Network Design with the Approach of Increasing the Quality of Products

Blood supply chain network design: a systematic review of literature and implications for future research

PurposeThe purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.Design/methodology/approachThe study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.FindingsThe results of the study present the researchers’ tendencies and preferences when designing their BSC network models.Research limitations/implicationsThe study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.Originality/valueThe study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

Multi-objective optimization model for blood supply chain network design considering cost of shortage and substitution in disaster

The problem of network design of blood supply chains is traditionally studied considering a maximum of three objective functions. In the real world, however, there are always many conflicting objectives for different stakeholders. This paper addresses a blood supply chain (BSC) network design problem to optimize the costs of blood shortage and substitution in addition to other common objective functions. To this end, four important objectives that decision makers are always faced with in disaster are considered: (1) minimizing the total cost, (2) minimizing transportation time, (3) minimizing total unsatisfied demand, and (4) maximizing the total reliability. A mixed-integer linear programming (MIP) model is proposed to formulate the problem at hand. Since this problem is known to be strongly NP-hard, the intelligent NSGA-II algorithm is applied to solve it in a reasonable time. Data from a real case study is used to evaluate the performance of the proposed solution method. The comparison of the results of the proposed algorithm with the mathematical model confirms the accuracy of the proposed method. Furthermore, the analysis of the results indicates the superiority of the proposed model over previous studies. Moreover, the proposed algorithm provides a wide range of suitable solutions. Therefore, different alternatives are presented to the decision makers to make a trade-off according to their preferences.

Development of a blood supply chain network design model by considering lateral transshipment and blood substitution

blood supply chain network design is an approach to design an efficient blood supply chain network. This study developed a model for determining the best Blood Supply Chain Network Design solution that considering costs and social sustainability. The developed model was divided into four echelons: blood donation centers, blood banks, regional hospitals, and demand points. The objective of this research was to minimize total supply chain costs while maximize job opportunities. This study considered the concept of lateral transshipment and blood substitution at the third echelon, namely the regional hospital, to ensure that demand was met. The concept of lateral transshipment enabled regional hospitals that having excess of blood unit to satisfy blood supplies to other regional hospitals. Furthermore, blood substitution was a response to emergency conditions, specifically a shortage of appropriate blood product units. Blood substitution scheme is allowed in this research, that is, each type of blood product with a certain group can not only meet its own demand, but can also be used as a replacement for the same product with other compatible groups. a multi-objective possibilistic mixed integer linear programming model was developed. According to the obtained results, the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created can be discovered by the model.

A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study

In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.

Optimizing the Issue of Blood Supply Chain Network Design with a Reliability Approach

Supply chain network design of a product is one of the primary and strategic measures in supply chain management that plays a vital role in supply chain performance. Since blood is a special commodity and has no substitute, supply chain management and optimization have a special place among researchers. In this research, for the first time, a two-objective nonlinear mixed integer model is presented to help make strategic and operational decisions in the blood supply chain. The first objective function is related to cost minimization and the second objective function is related to supply chain reliability maximization, which is considered as a series-parallel system. To check the validity of the model, a numerical example is solved using GAMS software, then using MOPSO meta-heuristic algorithm, the model is solved in larger dimensions and while comparing the performance criteria of multi-objective algorithms, the results are reviewed.

A bi-objective blood supply chain model under uncertain donation, demand, capacity and cost: a robust possibilistic-necessity approach

This paper addresses a multi-objective blood supply chain network design, considering economic and environmental aspects. The objective of this model is to simultaneously minimize a blood supply chain operational cost and its logistical carbon footprint. In order to embed the uncertainty of transportation costs, blood demand, capacity of facilities and carbon emission, a novel robust possibilistic-necessity optimization used regarding a hybrid optimistic-pessimistic form. For solving our bi-objective model, three multi-objective decision making approaches including LP-metric, Goal-Programming and Torabi- Hassini methods are examined. These approaches are assessed and ranked with respect to several attributes using a statistical test and TOPSIS method. Our proposed model can accommodate a wide range of decision-makers’ viewpoints with the normalized objective weights, both at the operational or strategic level. The trade-offs between the cost and carbon emission for each method has been depicted in our analyses and a Pareto frontier is determined, using a real case study data of 21 cities in the North-West of Iran considering a 12-month implementation time window.

A flexible robust model for blood supply chain network design problem.

World Health Organization (WHO) declared COVID-19 as a pandemic On March 12, 2020. Up to January 13, 2022, 320,944,953 cases of infection and 5,539,160 deaths have been reported worldwide. COVID-19 has negatively impacted the blood supply chain by drastically reducing blood donation. Therefore, developing models to design effective blood supply chains in emergencies is essential. This research offers a novel multi-objective Transportation-Location-Inventory-Routing (TLIR) formulation for an emergency blood supply chain network design problem. We answer questions regarding strategic, operational, and tactical decisions considering disruption in the network and blood shelf-life. Since, in real-world applications, the parameters of the proposed mathematical formulation are uncertain, two flexible uncertain models are proposed to provide risk-averse and robust solutions for the problem. We applied the proposed formulations in a case study. Under various scenarios and realizations, we show that the offered robust model handles uncertainties more efficiently and finds solutions that have significantly lower costs and delivery time. To make a reliable conclusion, we performed extensive worst-case analyses to demonstrate the robustness of the results. In the end, we provide critical managerial insights to enhance the effectiveness of the supply chain.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10479-022-04673-9.

A literature review on blood supply chain management focused on uncertainty: an inclusive approach

In the context of Blood Supply Chain Management, blood supply chain network design is one of the most pivotal planning problems. The stages of blood supply chain management comprise of blood collection, production, inventorying and distribution. The main challenges faced by supply channels are related to shortage, out datedness, and supply chain cost which needs to be minimized. In the current scenario, supply chain network design decisions should be flexible enough to operate under complex and uncertain business environments for many years. Decision-making under uncertainty is a crucial phenomenon and a large number of relevant publications have emphasized its importance. This paper makes an attempt towards reviewing the literature in the fields of blood supply chain network design under uncertainty. This study is organized into two phases. In the first phase, a discussion is made on the types of blood products, potential issues, and stages of blood supply chain management whereas in the second phase, an exploration is made on the optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, robust optimization and fuzzy mathematical programming. The scope of the study lies with capturing the unexplored dimensions related to blood supply chain management that will serve as substantial input for upcoming researchers and practitioners in the field of supply chain management.

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

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

Robust Box Approach for Blood Supply Chain Network Design under Uncertainty: Hybrid Moth-Flame Optimization and Genetic Algorithm

In this paper, a blood supply chain network (BSCN) is designed to reduce the total cost of the supply chain network under demand and transportation costs. The network levels considered for modeling include blood donation clusters, permanent and temporary blood transfusion centers, major laboratory centers and blood supply points. Other goals included determining the optimal number and location of potential facilities, optimal allocation of the flow of goods between the selected facilities and determining the most suitable transport route to distribute the goods to customer areas in uncertainty conditions. This study addresses the issue of blood prishability from blood sampling to distribution to customer demand areas. Given that the model was NP-hard, the MFGO algorithm were used to solve the model with a priority-based solution. The results of the design of the experiments showed the high efficiency of the MFGO algorithm in comparison with the PSO algorithm in finding efficient solutions. Also, the mean of the objective function in robust approach is more than the one in the deterministic approach, while the standard deviation of the first objective function in the robust approach is less than the one in the deterministic approach at all levels of the uncertainty factor.

A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation

A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation

A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation

A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation

A robust possibilistic programming approach for blood supply chain network design in disaster relief considering congestion

A robust possibilistic programming approach for blood supply chain network design in disaster relief considering congestion

Re-design of a blood supply chain organization with mobile units.

This research analyses the re-organization of a blood supply chain organization. Blood supply chain network design is a hard problem. Uncertainties of the blood supply and demand, perishability of blood over time and compatibility of blood types are some factors that make the problem difficult. This paper presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem. Unlike many studies on blood supply chain design in the literature, supply chain network consisting of mobile and permanent units is planned together effectively with our mixed-integer programming model. Multi-objective structure of the model minimizes distances between the blood supply chain elements and the length of the mobile unit routes. The objectives are prioritized by experts using the Analytical Hierarchical Process. Finally, the model is implemented on a real life case study using real data from the Eastern Anatolia region of Turkey for various supply demand scenarios. The solutions offered by the model are compared with the current situation in the region. It is shown that the proposed model gives at least %25 more effective solutions. Moreover, sensitivity analysis on the budget constraint is conducted, and robustness of the model is empirically illustrated.

An L-Shaped Method to Solve a Stochastic Blood Supply Chain Network Design Problem in a Natural Disaster

Level of blood and blood products in human body is very important. Therefor, managing supply of blood is critical issue in healthcare system specially when the system faced with high demand for the product. In natural disasters, demand for blood units increase sharply because of injuries. Hence, efficiency in blood supply chain management play a significant role in this situation in supplying blood for transfusion centers, it is vital to supply in right time to prevent from casualties. Present paper proposes an optimization model for designing blood supply chain network in case of an earthquake disaster. The proposed two-stage stochastic model is Programmed based on scenarios for earthquake in a populated mega-city. The designed network has three layers; first layer is donation areas, the second layer consists distribution centers and facilities and the last layer is transfusion centers. In proposed two-stage stochastic optimization model, decisions of locating permanent collection facilities and amount of each blood type pre-inventory are made in first stage and operation decisions that have dependent on possible scenarios are made in second stage. The model also considers the possibility of blood transfusion between different blood types and its convertibility to blood derivatives regarding medical requirements. In order to solve the proposed two-stage stochastic model, L-shaped algorithm, an efficient algorithm to solve scenario based stochastic models, has been used. In addition, application of the model and the algorithm tests with real data of likely earthquake in Tehran mega city (Densest city of Iran).

A lateral resupply blood supply chain network design under uncertainties

The vibrant subject of blood banking and blood distribution is addressed where lateral resupply of blood products is allowed. In this paper, we studied a blood supply chain network design consisting of four conventional levels each of which corresponding to one entity in the real world: donors, blood collection facilities, blood centers, and hospitals. The blood supply chain network considered two important properties: ABO-Rh factors and shelf lives of blood products. Moreover, an integrated inventory system for sharing hospitals’ inventory levels is considered to investigate the two performance indicators of the network in terms of cross-matching and outdated units. This type of inventory system is known as “Lateral Resupply” permitting a hospital to satisfy its demand by the other hospitals’ inventories in the absence of the required product at the blood center and its excess in any hospital. The proposed mixed-integer mathematical programming model entails three objectives referring to the three underlying concepts of sustainability, additionally, to cope with uncertainties and multi objectiveness, a scenario-based optimization approach and revised multi-choice goal programming technique were employed, respectively. Finally, the results confirmed that the lateral resupply virtue improved the performance indicators.

A robust framework for designing blood network in disaster relief: a real-life case

This article proposes a novel design of a disaster relief blood supply chain network with multiple echelons and multiple products. It incorporates: (1) the blood donors’ behavior and preference for the selection of facilities where the blood donation takes place, (2) an estimating the quantity of the injured people under each disaster scenario, (3) the inherent uncertainty in input parameters and (4) the remaining capacity for satisfying the demand affected by the disruption in blood facilities (i.e., both blood collection and production centers). To incorporate the utility of blood collection facilities, a utility assessment approach, called Logit model, is developed, which measures the donors’ preference. The disrupted capacity and the number of injured people under each disaster scenario are estimated based on the disaster severity, the distance from the disaster (particularly earthquake) epicenter and the civil infrastructure. The uncertainty of parameters (e.g., cost parameters as well as blood supply and demand) along with the disruption in blood facilities are two unavoidable concerns of disaster relief blood supply chain planning. Accordingly, to handle the parameters under mixed uncertainty, a new approach which is two-stage stochastic-robust is utilized. Finally, using real data of a case study in Iran, the presented formulation and its solution approach are examined. The obtained results demonstrate that the related managers should be aware of blood donors’ behavior, injured people in disaster, and the effect of disruption in the blood supply chain network design. Moreover, when it comes to the uncertainty of parameters, the obtained results from the robust model overcome those of deterministic one. Finally, based on findings, applicable managerial insights are derived for the decision-makers in blood supply chain network design for disaster relief cases.

A reactive phase against disruptions for designing a proactive platelet supply network

Previous studies in blood supply chain network design often follow a commonly used approach in protecting the chain against disruptions, considering the effects of disruptions on the designing phase. However, in many real-world situations, disruptions cannot be adequately measured in advance. Moreover, using disruptions in the designing phase through the common two-stage stochastic programming models impose high costs on the network, since they cannot be updated based on unpredicted disruptions. This paper proposes an updatable two-phase approach which deals with disruptions in the operational phase, not in the strategic design phase. In the first step, called the proactive phase, a nominal platelet supply chain network is designed under operational uncertainty, using the whole-blood collection method. In the event of disruptions, the second step, called the reactive phase, is applied, and the tailored network is updated based on the realized data, using apheresis as the collection mechanism. The operational risks are captured using a fuzzy programming approach in the model. Based on the real data from Fars province of Iran, we compare the performance of the two-phase approach with the commonly used approaches in the literature, resulting in more flexible decisions, and consequently, less conservatism degree rather than the existing approaches.