Blood Supply Chain Research Articles

Blood Supply Chain Optimization Model with Transshipment and Multiple Blood Products

Blood Supply Chain Optimization Model with Transshipment and Multiple Blood Products

A robust location-allocation model for optimizing a multi-echelon blood supply chain network under uncertainty

A robust location-allocation model for optimizing a multi-echelon blood supply chain network under uncertainty

An integrated blood supply chain network design during a pandemic

The emergency supply of blood in the wake of pandemic has proved challenging. We develop in this paper a bi-objective blood supply chain (BSC) model that uses two-stage stochastic optimisation to optimise the expected cost of the supply chain and the average blood delivery time when a pandemic occurs. The model considers not only vertically and horizontally integrated mechanisms of the BSC network, but also several specific assumptions about the pandemic, such as the risk of disruption in donor groups, and the restriction of ‘no cross blood donation’. To alleviate blood shortages in the context of a pandemic, we further extended our model, which allows for blood type substitution according to ABO/Rh(d)-compatibility. To efficiently solve the model and generate the Pareto front, we develop a hybrid solution algorithm to solve the proposed model, including the ε-constraint method to transform the bi-objective model into a single-objective model, the subgradient method for finding a lower bound, and two heuristic methods for finding an upper bound. Moreover, we perform extensive numerical studies to assess the performance of the proposed approach. We also discuss the impacts of the key parameters, integration mechanisms, and planning blood type substitution on the BSC. Finally, we also apply the proposed model to a real case study of the BSC in Chongqing, China.

A two-stage stochastic programming framework for blood product inventory management with ABO substitution and lateral transshipment

This work proposes a two-stage stochastic optimization model for managing the inventory of a two-echelon blood supply chain comprised of a blood center and a network of hospitals and transfusion points. The proposed model identifies optimal distribution quantities across the supply chain, considering demand stochasticity and product perishability. Furthermore, the model utilizes redistribution policies, such as lateral transshipment and product substitution based on ABO compatibility, as corrective actions to minimize outdates (i.e., expired product) and shortages across the network. Using a computational experiment that includes a network with one regional blood center and twenty hospitals, we show that inventory management policies that include both ABO substitution and transshipment can significantly decrease the expected total network costs. Similarly, we see corresponding reductions in the expected number of outdated units and emergency requests to external networks for additional supply to satisfy unmet demand.

Blood supply chain location-inventory problem considering incentive programs: comparison and analysis of NSGA-II, NRGA and electromagnetic algorithms

Blood supply chain location-inventory problem considering incentive programs: comparison and analysis of NSGA-II, NRGA and electromagnetic algorithms

Developing a multi-objective model for a multi-level supply chain of blood products under uncertainty and the global pandemic: a hybrid robust optimization approach

The global COVID-19 pandemic has caused a substantial decrease in the blood supply and its products as a vital commodity. It has had adversely affected on the activities of blood organizations and facilities as well as public health. In this critical situation, the particular supply and blood demand products have affected certain sensitive managerial decisions. The purpose of the present study is to develop a multi-objective formulation for a multi-level supply chain of blood products under uncertainty and global pandemic conditions. The modeling is based on three objectives: minimizing the costs of the entire blood supply chain network, minimizing the pandemic virus transmission by donors in each of the blood donation centers, and maximizing the attractiveness of the blood donation centers in order to optimize the blood supply chain conditions and meet the needs of patients in the best possible way. Besides, since uncertainty is an integral part of supply chains, an efficient combination of scenarios, intervals and fuzzy robust optimization approaches is applied. As the results show, using robust approaches to deal with uncertain parameters can provide excellent conditions for efficient responses to those who demand blood products as well as pandemic patients who need the plasma of survivors.

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.

Towards net zero healthcare transport operations in Indonesia: A total cost of ownership approach

Managing transport is key to support the achievement of net zero healthcare. This study aims to assess transport options (single vs. multi-modal; electric vs. combustion) considering sustainability, efficiency, and responsiveness of the healthcare operations. To achieve the aim, we develop a mathematical model and conduct numerical experiment using a total cost of ownership (TCO) approach and a case study of blood supply chain in Yogyakarta, Indonesia. We find that electric vehicle slightly outperforms combustion vehicle both in the single and multi-modal scenarios within optimised routes. We contribute by proposing the first TCO-based transport model and simulation for multivehicle operations within the healthcare sector. This study could help hospitals and blood centres conduct scenario planning to help achieve net zero targets, which is in line with the current trend set by the government to use more electric vehicles and the scarce use of electric vehicles in healthcare operations.

Blood Link

Abstract: In the face of medical emergencies, the rapid and reliable delivery of blood to hospitals can be a matter of life and death. This project presents "BloodLink," a comprehensive solution that leverages modern web technologies and drone delivery systems to streamline the blood supply chain between blood banks and hospitals. The front-end of the system is developed using HTML, CSS, and JavaScript, ensuring a user-friendly interface for hospital staff to post blood group requirements. The backend is powered by the Flask framework of Python, facilitating robust and scalable server-side operations. Utilizing the Google Maps API, our platform dynamically identifies and displays the nearest blood banks in response to hospital requests, ensuring timely and accurate information. For critical situations requiring immediate action, we have integrated an innovative drone delivery system designed to expedite the transportation of blood to the requesting hospitals. The software development phase of the project is complete, and efforts are currently focused on the practical design and implementation of the drone system. This paper details the architecture, development process, and potential impact of "BloodLink" in revolutionizing emergency blood delivery and hospital blood bank coordination.

Exploring the determinants of risk behavior for transfusion transmissible infections among first-time blood donors in Mandalay General Hospital, Myanmar.

Blood donation is vital to healthcare, but it must be kept safe by mitigating the risk of transfusion transmissible infections (TTIs). The objective of this study was to investigate the factors that influence risk behavior for transfusion transmissible infections among first-time blood donors at Mandalay General Hospital, Myanmar. This study utilized a cross-sectional study design using secondary data. Mandalay city and Mandalay Blood Bank in Mandalay General Hospital were purposely selected and a total of 406 first-time blood donors participated. A structured questionnaire administered by an interviewer was used. The questionnaire contained background characteristics, knowledge on TTIs, attitude toward TTIs, and TTIs risk behaviors. To examine the determinants (background characteristics, knowledge, and attitude) that affect risk behavior, inferential statistics techniques that included the chi-squared test, bivariable logistic regression, and multivariable logistic regression were applied. A p-value of less than 0.05 signified statistical significance. Among 406 first-time blood donors, 52.9% were under 20 years old, and 53.7% were male. Most had undergraduate education (77.6%), were married (84.2%), and were students (55.7%). Additionally, 76.8% hadn't received the hepatitis B vaccine. Blood groups were distributed as follows: B (40.0%), O (33.8%), A (23.4%), AB (8.9%). About 15.8% showed high knowledge level, and 63.6% had high attitude. Notably, 29.3% exhibited high-risk behavior for TTIs. Age was associated with lower risk behavior (OR = 1.54, 95% CI: 0.99, 2.38, p = 0.049), but lost significance in multivariable regression (p = 0.214). Knowledge on TTIs didn't show significance. However, high attitudes were significantly associated with lower risk behavior (OR = 11.4, 95% CI: 1.25, 103.83, p = 0.017, retained in multivariable regression, p = 0.012). Findings of this study contribute in the development of programs that ensure a safe and reliable blood supply chain. To improve blood safety standards among first-time blood donors, this study highlights the value of targeted education and screening processes, placing particular emphasis on acquiring knowledge and positive attitude toward blood donation and risk behavior.

Prioritizing Barriers to Resilience in Blood Supply Chains: An Integrated Multi-Criteria Decision-Making Approach

Prioritizing Barriers to Resilience in Blood Supply Chains: An Integrated Multi-Criteria Decision-Making Approach

A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach

Annually, different regions of the world are affected by natural disasters such as floods and earthquakes, resulting in significant loss of lives and financial resources. These events necessitate rescue operations, including the provision and distribution of relief items like food and clothing. One of the most critical challenges in such crises is meeting the blood requirement, as an efficient and reliable blood supply chain is indispensable. The perishable nature of blood precludes the establishment of a reserve stock, making it essential to minimize shortages through effective approaches and designs. In this study, we develop a mathematical programming model to optimize supply chains in post-crisis scenarios using multiple objectives. Presented model allocates blood to various demand facilities based on their quantity and location, considering potential situations. We employ real data from a case study in Iran and a robust optimization approach to address the issue. The study identifies blood donation centers and medical facilities, as well as the number and locations of new facilities needed. We also conduct scenario analysis to enhance the realism of presented approach. Presented research demonstrates that with proper management, crises of this nature can be handled with minimal expense and deficiency.

Developing an integrated blood supply chain network in disaster conditions considering multi-purpose capabilities

PurposeThis study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.Design/methodology/approachThis paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.FindingsThe findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.Originality/valueThe researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

Bloodmobile location selection for resilient blood supply chain: a novel spherical fuzzy AHP-integrated spherical fuzzy COPRAS methodology

PurposeBlood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.Design/methodology/approachThis study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.FindingsThe results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.Practical implicationsThis study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.Originality/valueThis study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

A Multiobjective Model for a Multilevel Blood Supply Chain to Improve the Attractiveness of Blood Centers during the COVID-19 Pandemic

Failure to control crisis conditions leads to irreparable damage to many supply chains around the world, including blood supply chains (BSCs) as critical networks in the health system. Consequently, it significantly reduces the supply of blood and its products, as vital materials, and exerts detrimental effects on the activities of blood organizations and facilities as well as the health of individuals in society. In the present study, the proposed model seeks to simultaneously minimize the operating costs and the shortage of blood products with the aim of improving the attractiveness of blood centers during the COVID-19 pandemic. Accordingly, by optimizing the overall cost and the attractiveness of blood donation centers, an attractive efficient environment is provided. It can help to remove barriers to blood donation and improve blood health. To this end, the model takes certain strategies into account for the proper establishment of new local blood collection centers (BCCs) and mobile BCCs. It also arranges suitable transportation vehicles for the efficient transfer of blood products to the provincial centers of the candidate country and sets various incentive policies for blood donation. In order to minimize the costs of the entire supply chain network and maximize the attractiveness of the BCCs, a two-objective mathematical model is developed. It produces Pareto solutions using the ε-constraint method. Finally, the efficiency of the proposed approach and the sensitivity of the corresponding parameters are analyzed through a practical case study. The obtained results represent that a growth in the attractiveness of blood centers induces a raise in the number of donors, and, consequently, the amount of the donated blood grows. This depends on more investment at all levels of the supply chain, including collection, production, storage, and transportation. Moreover, the performance and attractiveness of a BSC can be enhanced significantly if the number of collection centers and the amount of blood sent from the receiving centers to the demand nodes are increased.

PERANCANGAN STRATEGI MITIGASI RISIKO DAN KEY RISK INDICATORS PADA BLOOD SUPPLY CHAIN PMI KABUPATEN GUNUNGKIDUL

The concept of blood supply chain is distribute blood products safely, cheaply, and accessible to patients. There are four main activities in the blood supply chain, namely blood collection, blood processing, laboratory testing, and storage and distribution of blood products. The current development of PMI Gunungkidul Regency in carrying out its duties is still hampered by several things caused by risks, one of which is supply and demand uncertainty. In 2021, the blood supply at PMI will still experience a crisis of decreasing by 20%-30%, while the demand for blood from hospitals continues to exist and fluctuates. Based on the results of House of Risk Phase 1, there are 10 dominant risks that affect the blood supply chain. The results of the House of Risk Phase 2 resulted in preventative action to handle the 10 dominant risks. Apart from that, the results of the key risk indicators (KRI) are used as an early warning system for PMI Gunungkidul Regency. Implementation of preventive actions that have been designed and implemented key risk indicators, namely the number of blood bag supplies per month with a limit value of 79 blood bags and 399 blood bags.

A Two-Objective Model for the Multilevel Supply Chain of Blood Products with the Approach of Reducing the Rate of Contagion under the (COVID-19) Epidemic Outbreak Conditions

The conditions of the coronavirus epidemic have put much pressure on the healthcare system. This disease has hurt the blood supply through the reduction of blood donation and the reduction of access to suitable collection facilities due to dysfunction. Considering the importance of the subject, the purpose of this paper is to design a two-level supply chain network for blood products with the approach of reducing costs and the rate of contagion under the conditions of epidemic outbreaks (COVID-19). After examining the solution methods for multilevel supply chain networks of blood products under the conditions of the spread of the COVID-19 virus, three exact solution methods, including LP-metric, an improved version of the augmented ε-constraint (AUGMECON2), and an improved weighted Chebyshev, are proposed. They are used to solve the model in small dimensions. In order to compare the methods in the obtained solutions, several numerical examples of different sizes are generated and solved. Then, using the statistical assumption test, the obtained results are compared in all numerical examples by Tukey’s technique. Also, the TOPSIS is applied to select the best method. Finally, in order to investigate the reaction of the objectives to the changes in the contagion probability parameter, a sensitivity analysis has been performed. The results emphasize that improving the performance of the blood supply chain (BSC) can lead to a reduction in BSC costs and improved service to patients. Also, the adaptation of different components of the BSC and regular coordination between them play an efficient role in controlling and improving this disease and reducing the costs of the BSC. Also, receiving the plasma product of recovered people from type (II) donors can play a vital role in reducing the percentage of disease transmission.

Extensive red blood cell matching considering patient alloimmunization risk.

Red blood cell (RBC) transfusions pose a risk of alloantibody development in patients. For patients with increased alloimmunization risk, extended preventive matching is advised, encompassing not only the ABO-D blood groups but also the most clinically relevant minor antigens: C, c, E, e, K, Fya, Fyb, Jka, Jkb, S and s. This study incorporates patient-specific data and the clinical consequences of mismatching into the allocation process. We have redefined the MINimize Relative Alloimmunization Risks (MINRAR) model to include patient group preferences in selecting RBC units from a finite supply. A linear optimization approach was employed, considering both antigen immunogenicity and the clinical impact of mismatches for specific patient groups. We also explore the advantages of informing the blood bank about scheduled transfusions, allowing for a more strategic blood distribution. The model is evaluated using historical data from two Dutch hospitals, measuring shortages and minor antigen mismatches. The updated model, emphasizing patient group-specific considerations, achieves a similar number of mismatches as the original, yet shifts mismatches among patient groups and antigens, reducing expected alloimmunization consequences. Simultaneous matching for multiple hospitals at the distribution centre level, considering scheduled demands, led to a 30% decrease in mismatches and a 92% reduction in shortages. The reduction of expected alloimmunization consequences by incorporating patient group preferences demonstrates our strategy's effectiveness for patient health. Substantial reductions in mismatches and shortages with multi-hospital collaboration highlights the importance of sharing information in the blood supply chain.

Presenting a mathematical model of blood supply chain considering the efficiency of collection centers and development of metaheuristic algorithm in M/M/C/K queuing system.

In this study, a multiobjective model was devoted to the objectives of minimizing blood supply chain costs and minimizing the waiting time of blood donors for blood transfusion and minimizing blood transfusion schedule and increasing the efficiency of fixed and mobile centers in collecting blood. One of the most important constraints considered in the mathematical model is the capacity constraints of considering fixed and mobile blood facilities and management of the transfer of blood products to centers for collecting and distinguishing healthy and unhealthy blood. A multiobjective model was considered with the objectives of minimizing blood supply chain costs, the waiting time of blood donors for blood transfusion, and blood transfusion timing and increasing the efficiency of fixed and mobile centers in blood collection. The model findings were analyzed in order to validate the model on a larger scale, using the meta-innovative algorithm NSGAII and MOSPO. According to the research findings, we suggest that fuzzy uncertainty and fair distribution problem shouldn't be added to the dimensions of the main problem, and further analysis should be done in this area. It was shown that the NSGAII algorithm's performance was better than the MOPSO meta-heuristic algorithm.

Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty

Dynamic blood collection planning is crucial for adjusting strategies in response to varied donors' behavior within the dynamic urban environment, which can be utilized to mitigate wastage and scarcity due to the uncertain supply chain parameters. In this study, we address the challenge of adapting collection planning to dynamic fluctuations in potential blood donors’ behavior by introducing an innovative updatable approach based on the rolling horizon planning approach, which considers uncertain conditions in daily urban life. Taking into account one-day planning and diverse urban areas contexts, we propose an approach that operationally updates relocation and collection strategies throughout the daily fluctuation in urban conditions. We present a robust optimization approach to mitigate uncertainty across various parameters such as donors' behavior, demand, transportation time, and operational cost. Given the impact of dynamic and diverse conditions, we implement our updatable approach within the Tehran blood supply chain, which possesses the largest blood network in northern Iran, providing support to 108 hospitals and numerous laboratories. Deriving insights from our results, we prove a noteworthy 9.65% enhancement in blood collection across the entire network resulting from the implementation of the rolling horizon planning approach. Moreover, adjusting the dynamic conditions, in contrast to non-dynamic conditions, has led to an additional 24.14% increase in blood collection through temporary collection facilities as a main focus in this investigation.