Risk Factors Analysis in Blood Supply Chain: A Fuzzy Cognitive Mapping Approach
محورهای موضوعی : Supply Chain ManagementZeinab Kazemi 1 , Mahdi Homayounfar 2 , Mehdi Fadaei 3 , Mansour Soufi 4 , Ali Salehzadeh 5
1 - PhD Candidate, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
3 - Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
4 - Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
5 - Assistant Professor, Department of Biology, Rasht Branch, Islamic Azad University, Rasht, Iran
کلید واژه: Risk factor, Blood supply chain, FCM,
چکیده مقاله :
The blood supply chain (BSC) is a critical component of healthcare systems, where efficiency and reliability are paramount to ensuring timely and safe delivery of blood products to patients in need. Risk factors as the factors that directly affect the BSC could be considered permanently to ensure BSC’s productivity. So, understanding and managing these risks is vital for ensuring a robust and resilient blood supply chain. This research employs the Fuzzy Cognitive Map (FCM) approach to identify and analyze key risk factors affecting the blood supply chain. The required data was gathered using pairwise comparison questionnaire from 10 experts of the regional office of the Blood Transfusion Organization in Tehran province. By mapping the complex interrelationships between various risks, the study reveals that "Delays in Allocation and Distribution," "Disruptions in Logistics Processes," and "Blood Shortages" are among the most influential factors, with significant implications for the overall performance of the supply chain. The analysis also highlights the importance of "Weak Collaboration" and "Insufficient Capacity," which exacerbate operational inefficiencies. The findings suggest that addressing these risks through enhanced collaboration, capacity building, and the integration of advanced technologies can substantially improve the resilience and effectiveness of blood supply chains. Furthermore, the study offers strategic recommendations and suggests avenues for future research,
The blood supply chain (BSC) is a critical component of healthcare systems, where efficiency and reliability are paramount to ensuring timely and safe delivery of blood products to patients in need. Risk factors as the factors that directly affect the BSC could be considered permanently to ensure BSC’s productivity. So, understanding and managing these risks is vital for ensuring a robust and resilient blood supply chain. This research employs the Fuzzy Cognitive Map (FCM) approach to identify and analyze key risk factors affecting the blood supply chain. The required data was gathered using pairwise comparison questionnaire from 10 experts of the regional office of the Blood Transfusion Organization in Tehran province. By mapping the complex interrelationships between various risks, the study reveals that "Delays in Allocation and Distribution," "Disruptions in Logistics Processes," and "Blood Shortages" are among the most influential factors, with significant implications for the overall performance of the supply chain. The analysis also highlights the importance of "Weak Collaboration" and "Insufficient Capacity," which exacerbate operational inefficiencies. The findings suggest that addressing these risks through enhanced collaboration, capacity building, and the integration of advanced technologies can substantially improve the resilience and effectiveness of blood supply chains. Furthermore, the study offers strategic recommendations and suggests avenues for future research,
Aghsami, A., Abazari, S. R., Bakhshi, A., Yazdani, M. A., Jolai, S., & Jolai, F. (2023). A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems. Healthcare Analytics, 3, 100136.
Ala, A., Simic, V., Bacanin, N., & Babaee Tirkolaee, E. (2024). Blood supply chain network design with lateral freight: A robust possibilistic optimization model. Engineering Applications of Artificial Intelligence, 133(Part A), 108053.
Asadpour, M., Olsen, T. L., & Boyer, O. (2022). An updated review on blood supply chain quantitative models: A disaster perspective. Transportation Research Part E: Logistics and Transportation Review, 158, 102583.
Beliën, J., & Force, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.
Dehghani, M., Abbasi, B., & Oliveira, F. (2019). Proactive transshipment in the blood supply chain: A stochastic programming approach. Omega, 23, 1245-1253.
Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27–41.
Dutta, P., & Nagurney, N. (2019). Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers. Operations Research for Health Care, 23, 100230.
Entezari, S., Abdolazimi, O., Fakhrzad, M. B., Shishebori, D., & Ma, J. (2024). A bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics. Computers & Industrial Engineering, 188, 109899.
Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Hassanzadeh Amin, S. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236–250.
Ezugwu, A. E., Micheal, M. O., & Govender, P. (2019). Mathematical model formulation and hybrid metaheuristic optimization approach for near-optimal blood assignment in a blood bank system. Expert Systems with Applications, 137, 74-99.
Fahimnia, B., Jabbarzadeh, A., & Ghavamifar, A. (2018). Risk management in blood supply chains under emergency conditions. European Journal of Operational Research, 267(2), 1031-1047.
Fallahi, A., Mokhtari, H., & Akhavan Niaki, S. T. (2021). Designing a closed-loop blood supply chain network considering transportation flow and quality aspects. Sustainable Operations and Computers, 2, 170-189.
Fanoodi, M., Malmir, B., & Firouzi Jahantigh, F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in Biology and Medicine, 113, 103415.
Ghatreh Samani, M. R., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450–471.
Ghatreh Samani, M. R., Torabi, S. A., & Hosseini-Motlagh, S. M. (2018). Integrated blood supply chain planning for disaster relief. International Journal of Disaster Risk Reduction, 27, 168-188.
Ghorashi, S. B., Hamedi, M., & Sadeghian, R. (2020). Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computation & Applications, 32, 12173–12200.
Gilani Larimi, N., & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & Industrial Engineering, 137, 106014.
Gilani Larimi, N., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-Economic Planning Sciences, 68, 100697.
Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129-141.
Habibi-Kouchaksaraei, M., Paydar, M. M., & Asadi-Gangraj, E. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling, 55, 583-599.
Habibi-Kouchaksaraei, A., & Kazemi, A. (2020). Financial challenges in maintaining an efficient blood supply chain. Journal of Health Economics and Outcomes Research, 8(1), 29-37.
Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence, 90, 103493.
Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Cheraghi, S. (2019). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Faraji, M. (2024). Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty. Computers & Industrial Engineering, 188, 109908.
Jacobs, D. A., Silan, M. N., & Clemson, B. A. (1996). An analysis of alternative locations and service areas of American Red Cross blood facilities. Interfaces, 26(3), 40-50.
Kazemi, Z., Homayounfar, M., Fadaei, M., Soufi, M., & Salehzadeh, A. (2024). Multi-objective Optimization of Blood Supply Network Using the Meta-Heuristic Algorithms. Journal of Optimization in Industrial Engineering, (In Press), xx-xx.
Kenan, N., & Diabat, A. (2022). The supply chain of blood products in the wake of the COVID-19 pandemic: Appointment scheduling and other restrictions. Transportation Research Part E, 159, 102576.
Khalil, A., et al. (2020). Challenges in recruiting voluntary blood donors: A systematic review. Transfusion Medicine Reviews, 34(3), 173-179.K
Khalilpour Azari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Annals of Operations Research, 289, 123–152.
Kharaghani, M., Homayounfar, M., & Taleghani, M. (2023). Value Chains Analysis: Application of Fuzzy Cognitive Map in Pharmaceutical Industry. Journal of System Management, 10(2), 63-78.
Liu, X., & Song, X. (2019). Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-Papers OnLine, 52(13), 778-783.
Mansur, A., Handayani, D. I., Wangsa, I. D., Utama, D. M., & Jauhari, W. A. (2023). A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types. Decision Analytics Journal, 8, 100279.
Meneses, M., Santos, D., & Barbosa-Póvoa, A. (2023). Modelling the blood supply chain. European Journal of Operational Research, 307(2), 499-518.
Nezamoddini, N., Gholami, A., & Aqlan, F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks. International Journal of Production Economics, 225, 107569.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2018). Whole blood or apheresis donations? A multi-objective stochastic optimization approach. European Journal of Operational Research, 266(1), 193-204.
Pirabán, A., Guerrero, W. J., & Labadie, N. (2019). Survey on blood supply chain management: Models and methods. Computers and Operations Research, 112, 104756.
Rajendran, S., & Ravindran, S. R. (2019). Inventory management of platelets along blood supply chain to minimize wastage and shortage. Computers & Industrial Engineering, 130, 714–730.
Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2019). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research, 283, 679–703.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283, 1413–1462.
Setiawati, M., Simatupang, T. M., & Okdinawati, L. (2024). A dynamics approach for maturity assessment of hospital supply chain management. Journal of Optimization in Industrial Engineering, 17(1), 87-102.
Sharifi, M., & Ghaneipour, M. (2019). Technological challenges in Iran's blood supply chain. Iranian Journal of Blood and Cancer, 11(2), 79-85.
Shih, H., Kasaie, A., & Rajendran, S. (2023). A multiple criteria decision-making model for minimizing platelet shortage and outdating in blood supply chains under demand uncertainty. Healthcare Analytics, 3, 100180.
Suen, T. Y., Song, X., & Jones, D. (2023). A two-stage stochastic model for a multi-objective blood platelet supply chain network design problem incorporating frozen platelets. Computers & Industrial Engineering, 185, 109651.
Tavakol, P., Nahavandi, B., & Homayounfar, M. (2023). A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in COVID-19 pandemic. Journal of Optimization in Industrial Engineering, 34, 105–118.
Torrado, A., & Barbosa-Póvoa, A. (2022). Towards an optimized and sustainable blood supply chain network under uncertainty: A literature review. Cleaner Logistics and Supply Chain, 3, 100028.
Wang, C., & Chen, S. (2020). A distributionally robust optimization for blood supply network considering disasters. Transportation Research Part E: Logistics and Transportation Review, 134, 101840.
Wang, K. M., & Ma, Z. J. (2015). Age-based policy for blood transshipment during blood shortage. Transportation Research Part E: Logistics and Transportation Review, 80, 166-183.
World Health Organization (2017). Global status report on blood safety and availability 2016. WHO Press.
Islamic Azad University Rasht Branch ISSN: 2588-5723 E-ISSN:2008-5427
|
|
Optimization Iranian Journal of Optimization Volume 15, Issue 3, 2023, 183- 197 Research Paper |
|
Online version is available on: www.ijo.rasht.iau.ir
Risk Factors Analysis in Blood Supply Chain: A Fuzzy Cognitive Mapping Approach
Zeinab Kazemi1, Mahdi Homayounfar2*, Mehdi Fadaei 3, Mansour Soufi4 and Ali Salehzadeh5
1 PhD Candidate, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
2* Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
3 Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
4 Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran
5 Assistant Professor, Department of Biology, Rasht Branch, Islamic Azad University, Rasht, Iran
Revise Date: 02 October 2024 Abstract
Keywords: Risk factor Blood supply chain FCM |
INTRODUCTION
The blood supply chain (BSC) plays a critical role in the healthcare system, ensuring that blood and blood products are available to meet the needs of patients across the country. However, the BSC faces several challenges that make its management particularly complex. These challenges include uncertain supply and demand, high service level requirements (Meneses et al., 2023). In addition to these challenges, the BSC must also address the perishability of blood products, which adds further complexity to supply chain management (Toude Bahambari and Soufi, 2021). Blood is a degradable product, and only a small fraction of donated blood is usable due to specific storage and processing requirements. For example, red blood cells have a shelf life of 35 to 42 days, platelets last only 5 to 7 days, and plasma can be stored for 1 to 3 years under specific conditions (Pirabán et al., 2019; Kazemi et al., 2024). This perishability necessitates precise planning and coordination across multiple facilities to avoid shortages, wastage, and to ensure that blood is available at the right time and place (Beliën & Forcé, 2012). Perishability of blood products enhances complexity to the supply chain (SC), making difficult the determination of optimal quantities of blood to be available for medical treatments (e.g., cancer, anemia), organ transplants, surgeries (e.g., open-heart surgery), or emergencies (Kazemi et al., 2024).
Given these complexities, efficient blood supply chain management is crucial. The BSC in Iran is a multi-facility and multi-product network that must comply with strict safety and public health directives. The blood supply chain is generally divided into 5 echelons: (1) blood collection, (2) blood testing, (2) blood processing, (4) blood storage, and (5) blood distribution. The process of receiving blood from donors is called blood collection. When blood is collected, should be tested to screen its viruses and diseases in terms of blood testing (. Next, in the processing phase, whole blood should be processed to separate its derivations such as red blood cells (RBC), plasma, and platelets. Once available for use, the blood derivations are allocated to inventories, typically at blood banks and hospitals, being ready to be distributed and used. The process of supplying the amount of blood required to satisfy the demand is examined during the distribution phase (Dillon et al., 2017; Pirabán et al., 2019).
Managing the BSC encompasses the major challenge of balancing storage and wastage of the blood units. Given the perishable nature of this product, storing an excessive number of blood units could result in the wastage of this limited resource. On the other hand, having shortages may result in tragic outcomes since lives can be lost if no stock is available when it is needed (Dillon et al., 2017). The BSC must be both efficient and sustainable in these six processes to guarantee the availability of blood for medical treatments, encompassing everything from blood collection from donors to transfusion to patients (Osorio et al., 2018).
The Iranian Blood Transfusion Organization (IBTO), established in 1974, has developed into a comprehensive system responsible for collecting, processing, and distributing blood and blood products nationwide. Iran's reliance on voluntary, non-remunerated blood donations is a key factor in ensuring the safety and reliability of its blood supply (WHO, 2017). However, the country faces significant challenges, including a high prevalence of traffic accidents, which placed Iran in the 7th rank among the ten most accident-prone countries between 1999 and 2018, leading to increased blood demand for trauma and surgeries (Kazemi et al., 2024). Despite this, only about 5% of the population donates blood, which has resulted in shortages that complicate the management of blood supply and increase the risk of higher fatality rates (Rajendran & Ravindran, 2019).
Besides the donations uncertainty, risks created from natural disasters, epidemic diseases such as COVID-19, increase this uncertainty. The majority of studies on BSC problems do not dedicate enough attention to these risks that disturb the decision-maker on the design, planning, and operations of a BSC (Samani & Hosseini-Motlagh, 2019). This paper addresses these gap in the literature by presenting fuzzy cognitive mapping (FCM) approach for analyzing the complex BSC systems with high interactions. Because of FCM’s capabilities for analyzing feedback structures, dealing with qualitative variables, considering direct and indirect relationships between variables, modelling systems where explicit knowledge is limited but expert (implicit) knowledge is available, and its simplicity and accuracy in comparison to other methods, this paper applied FCM to analyze the main risks that affect the blood supply network in Iran.
The remainder of the paper is structured as follows: Section 2 reviews the recent literature on the blood supply chain, Section 3 outlines the research methodology and proposed framework, Section 4 presents the developed model and discusses its application in a real case study, and Section 5 concludes with recommendations for future research.
LITERATURE REVIEW
Blood supply chain studies began in the 1960s. Jacobs et al. (1996) presented a model in their research. In the field of location-allocation of facilities in the blood supply chain and relocation and network configuration and facilities (Beliën and Force, 2012). Wang and Ma (2015) proposed an approach based on the age of blood units in which they compared the two modes of age and the amount of inventory in the blood bank of hospitals, due to decreasing inventory or increasing the age of blood units exchanged between hospitals when there is a shortage. Habibi-Kouchaksaraei et al. (2018) proposed a robust optimization model for designing a bi-objective multi-period three echelons supply chain network of blood in a disaster. The proposed model determines the number and location of facilities and the best strategy to allocate them under three different scenarios, while the goals are to minimize costs and shortages of blood.
Dutta and Nagurney (2019) proposed a multitiered competitive supply chain network model for the blood banking industry, that captures the economic interactions among three blood service organizations, the hospitals or medical centers, and the player groups. They modeled the behavior of each category and used the theory of variational inequalities to derive the equilibrium conditions for the entire supply chain. Hosseini Motlagh et al. (2019) developed a bi-objective MLP model to design the collection, production, and distribution network of blood under uncertain conditions and through a multi-period planning horizon. In a research, Liu and Song (2019) proposed a discrete-time MILP model to cope with the underlying uncertainty through a rolling horizon approach in order to optimally manage the blood supply chain system in disaster relief. Their model also takes into account blood characteristics and blood emergency supply constraints.
Khalilpour Azari et al. (2020) developed an efficient model for a 6 echelon BSC which consists of donors, blood collection centers (permanent and temporary), regional blood centers, local blood centers, regional hospitals, and local hospitals. The solution aims to avoid the worst consequences of a disaster using a neural-learning process. Fallahi et al. (2021) formulated a closed-loop BSC that considers blood transportation equipment and the relevant quality features. Then, they used a differential evolution algorithm for solving the problem. Kenan and Diabat (2022) using two-stage stochastic programming formulated the BSC problem in disasters considering the uncertainty of both supply and demand. Solving the model with heuristic algorithms showed that bigger capacities of permanent collection facilities are favored over the mobility of temporary facilities. Suen et al. (2023) proposed a two-stage stochastic programming model to investigate the opportunity of incorporating frozen PLTs into the PLT supply chain. To investigate a more realistic situation when clear targets of blood shortage, wastage and substitution penalties are available, an extended goal programming model is built based on the proposed model. Aghsami et al. (2023) developed a mathematical model to minimize the total costs and maximize satisfaction of donors by waiting time reduction in a BSC system. They presented and solved a real-world case study using a new meta-heuristic algorithm to illustrate the model’s applicability. Hosseini-Motlagh et al. (2024) investigated 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. they 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. Ala et al. (2024) proposed a multi-objective BSC network design problem that aims to reduce the cost of establishing fixed and temporary facilities, transferring blood products, and the amount of shortage. They used a robust possibilistic MILP method in order to deal with distribution and locational decisions. Table 1 summarized some of the studies on BSC where conducted in the last 5 years.
Table 1: Recent studies on BSC
Reference | Title |
|---|---|
Fanoodi et al. (2019) | Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models |
Ghatreh Samani et al. (2019) | A multilateral perspective towards blood network design in an uncertain environment methodology and implementation |
Gilani Larimi and Yaghoubi (2019) | A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies |
Rajendran and Ravindran (2019) | Inventory management of platelets along blood supply chain to minimize wastage and shortage |
Pritha and Nagurney (2019) | MultitieRB supply chain network competition: Linking blood service organizations, hospitals, and payers |
Liu and Song (2019) | Emergency Operations Scheduling for a Blood Supply Network in Disaster Reliefs |
Salehi et al. (2019) | Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran |
Hosseini-Motlagh et al. (2019) | Robust and stable flexible blood supply chain network design under motivational initiatives |
Dehghani et al. (2019) | Proactive transshipment in the blood supply chain: A stochastic programming approach |
Ezugwu et al. (2019) | Mathematical model formulation and hybrid metaheuristic optimization approach for near-optimal blood assignment in a blood bank system |
Gilani Larimi et al. (2019) | Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories |
Samani (2019) | An enhanced procedure for managing blood supply chain under disruptions and uncertainties |
Haghjoo et al. (2020) | Reliable blood supply chain network design with facility disruption: A real-world application |
Wang and Chen (2020) | A distributionally robust optimization for blood supply network considering disasters |
Ghorashi et al. (2020) | Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO |
Hamdan and Diabat (2020) | Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation |
Khalilpour Azari et al. (2020) | Designing an efcient blood supply chain network in crisis: neural learning, optimization and case study |
Nezamoddini et al. (2020) | A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks |
Fallahi et al. (2021) | Designing a closed-loop blood supply chain network considering transportation flow and quality aspects |
Kenan and Diabat (2022) | The supply chain of blood products in the wake of the COVID-19 pandemic: Appointment scheduling and other restrictions |
Torrado and Barbosa-Póvoa (2022) | Towards an Optimized and Sustainable Blood Supply Chain Network under Uncertainty: A Literature Review |
Asadpour et al. (2022) | An updated review on blood supply chain quantitative models: A disaster perspective |
Mansur et al. (2023) | A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types |
Suen et al. (2023) | A two-stage stochastic model for a multi-objective blood platelet supply chain network design problem incorporating frozen platelets |
Meneses et al. (2023) | Modelling the Blood Supply Chain |
Aghsami et al. (2023) | A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems |
Shih et al. (2023) | A multiple criteria decision-making model for minimizing platelet shortage and outdating in blood supply chains under demand uncertainty |
Hosseini-Motlagh et al. (2024) | Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty |
Entezari et al. (2024) | A Bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics |
Ala et al. (2024) | Blood supply chain network design with lateral freight: A robust possibilistic optimization model |
Kazemi et al. (2024) | Multi-objective Optimization of Blood Supply Network Using the Meta-Heuristic Algorithms |
Reviewing the literature, it did not find any research which considers risk analysis in blood supply chain with an analytical fuzzy approach.
RESEARCH METHOD
This study applied a systematic approach namely fuzzy cognitive mapping for analyzing the risk factors of blood supply chain in Iran. The required data to implement the real world problem, attained from experts of regional office of the Blood Transfusion Organization in Tehran province.
FCM is chosen for its holistic way of analyzing the problem and the potential success factors affecting the system and developing improvement strategies. So, the initial step involves a thorough review of existing literature to identify key risk factors that impact the blood supply chain. This review focused on previous studies and reports, leading to the identification of 14 primary risk factors. The aggregated expert opinions were then used to construct an adjacency matrix, reflecting the relationships between BSC risk factors. Finally, the FCM technique was employed to model the interactions between risk factors within the blood supply chain. This involved analyzing the influence, sensitivity, and prioritization of each risk factor using static analysis outputs. The FCM was visually represented as a graphical model, highlighting the interrelationships between factors. Dynamic behavior analysis was then conducted using activation functions, which allowed for the examination of the system's response to various scenarios. This step also demonstrated the FCM's capability to simulate changes in risk factors and assess their overall impact on BSC. Fig. 1 illustrates the steps of risk factors’ evaluation in BSC.
Fig 1. Research framework
FUZZY COGNITIVE MAP (FCM)
FCM first introduced by Kosco (1986), is a powerful method used to model and analyze complex systems with high levels of interaction between components. The reasoning process of fuzzy cognitive mapping is based on neuro-fuzzy system (Movahedi et al., 2022). Actually, FCM consists of a set of neural processing entities called concepts (neurons) and the causal relations among them. The activation value of such neurons regularly takes values in the [0, 1] interval, so the stronger the activation value of a neuron, the greater its impact on the network. Also, connected weights are relevant in this scheme. The strength of the causal relation between two neurons Ci and Cj is quantified by a numerical weight wij ∈ [−1, 1].
There are three types of causal relationships between neural units in an FCM, being detailed as follows (Kharaghani et al. 2024; Tavakkol et al., 2023):
· wij > 0 indicate a positive causality,
· wij < 0 indicate a negative causality,
· wij = 0 indicate no causality.
Eq. 1 formalizes Kosko’s activation rule, with A (0) as the initial value. A new activation vector is calculated at each step t and after a fixed number of iterations the FCM will be at one of the following states: (i) equilibrium point, (ii) limited cycle or (iii) chaotic behavior. The FCM is said to have converged if it reaches a fixed-point attractor, otherwise the updating process terminates after a maximum number of iterations T is reached.
| (1) |
| (2) |
| (3) |
Table 2: Main risk factors of the blood supply chain
Risk Factor | Reference |
|---|---|
Blood Shortages | Pirabán et al., 2019; Khalili et al., 2020; Ghatreh Samani et al., 2018; Fahimnia et al., 2018; Nezamoddini et al., 2020; Nezamoddini et al., 2020 |
Blood Wastage | Ghatreh Samani et al., 2018; Fahimnia et al., 2018; Habibi-Kouchaksaraei et al., 2018; Khalili et al., 2020; |
Blood Products Perishability | Kazemi et al., 2024; Sharifi et al., 2019; Hamdan & Diabat, 2020; Nezamoddini et al., 2020; |
Demand and Supply Uncertainty | Meneses et al., 2023; Ghatreh Samani et al., 2018; Fahimnia et al., 2018; Khalili et al., 2020; Nezamoddini et al., 2020 |
Infrastructure and Technological Limitations | WHO, 2017; Khalili et al., 2020; Sharifi et al., 2019; Nezamoddini et al., 2020 |
Delays in Allocation and Distribution | Nezamoddini et al., 2020; Hamdan & Diabat, 2020; Eskandari-Khanghahi & Ghatreh Samani, 2018; |
Disruptions in Logistics Processes | Hamdan & Diabat, 2020; Khalili et al., 2020; Sharifi et al., 2019; Ghatreh Samani et al., 2018; Fahimnia et al., 2018 |
Complexities in Inventory Processes | Sharifi & Ghaneipour, 2019; Nezamoddini et al., 2020; Khalili et al., 2020; |
Exceeding Blood Requisition in Actual Usage | Habibi-Kouchaksaraei & Kazemi, 2020; Sharifi et al., 2019; Ghatreh Samani et al., 2018; Fahimnia et al., 2018; Nezamoddini et al., 2020 |
Weak Collaboration | Fahimnia et al., 2018; Khalili et al., 2020; Nezamoddini et al., 2020 |
Problems in Quality Control | Nezamoddini et al., 2020; Rajendran & Ravindran, 2019; Khalili et al., 2020; Hamdan & Diabat, 2020 |
Weak Screening | Hamdan & Diabat, 2020; Sharifi et al., 2019; Ghatreh Samani et al., 2018; Fahimnia et al., 2018; Nezamoddini et al., 2020 |
Insufficient Capacity | Ghatreh Samani et al., 2018; Hamdan & Diabat, 2020; Fahimnia et al., 2018; Khalili et al., 2020; Sharifi et al., 2019; Nezamoddini et al., 2020 |
Problems in Safety Issue | Khalili et al., 2020; Sharifi et al., 2019; Ghatreh Samani et al., 2018; Fahimnia et al., 2018 |
After identifying the affecting factors on VC, they must be evaluated by the experts. For this purpose, a questionnaire was designed based on the factors in Table 3; then, the 20 selected indicators were mentioned in the first row and column of the questionnaire table, and the experts were asked to determine the intensity of causal relationships between the factors based on the linguistic variables mentioned in Table 2. Since the judgments of the experts were ambiguous and uncertain, the linguistic variables in this study were converted to triangular fuzzy numbers. Next, the fuzzified matrixes of the experts’ judgments were obtained and their average is calculated in form of the "aggregated adjacency matrix". Table 2 demonstrates this matrix.
Table 2: Aggregated adjacency matrix for BSC risk factors
Factors |
| F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | F10 | F11 | F12 | F13 | F14 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Blood Shortages | F1 | - | -0.12 | 0.00 | 0.00 | 0.00 | 0.67 | 0.16 | 0.00 | 0.00 | 0.19 | 0.00 | 0.00 | -0.25 | 0.00 |
Blood Wastage | F2 | 0.44 | - | 0.00 | 0.00 | 0.00 | 0.00 | 0.11 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.17 | 0.12 |
Blood Products Perishability | F3 | 0.51 | 0.26 | - | 0.00 | 0.00 | 0.00 | 0.00 | 0.62 | 0.00 | 0.00 | 0.00 | 0.00 | -0.19 | 0.35 |
Demand and Supply Uncertainty | F4 | 0.67 | 0.40 | 0.00 | - | 0.00 | 0.60 | 0.51 | 0.23 | 0.00 | 0.35 | 0.00 | 0.00 | 0.21 | 0.09 |
Infrastructure and Technological Limitations | F5 | 0.20 | 0.28 | 0.16 | 0.00 | - | 0.35 | 0.68 | 0.00 | 0.00 | 0.36 | 0.27 | 0.20 | 0.62 | 0.37 |
Delays in Allocation and Distribution | F6 | 0.39 | 0.24 | 0.45 | 0.21 | 0.00 | - | 0.43 | 0.00 | 0.19 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 |
Disruptions in Logistics Processes | F7 | 0.53 | 0.36 | 0.22 | 0.00 | 0.00 | 0.79 | - | 0.00 | 0.00 | 0.30 | 0.00 | 0.00 | 0.00 | 0.35 |
Complexities in Inventory Processes | F8 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.18 | 0.00 | - | 0.00 | 0.18 | 0.00 | 0.00 | 0.00 | 0.00 |
Exceeding Blood Requisition in Actual Usage | F9 | 0.25 | 0.35 | 0.00 | 0.23 | 0.00 | 0.00 | 0.24 | 0.00 | - | 0.26 | 0.00 | 0.00 | -0.18 | 0.25 |
Weak Collaboration | F10 | 0.31 | 0.16 | 0.22 | 0.24 | 0.00 | 0.49 | 0.50 | 0.00 | 0.11 | - | 0.00 | 0.00 | 0.33 | 0.29 |
Problems in Quality Control | F11 | 0.00 | 0.39 | 0.18 | 0.00 | 0.00 | 0.15 | 0.00 | 0.00 | 0.00 | 0.00 | - | 0.55 | 0.00 | 0.46 |
Weak Screening | F12 | 0.00 | 0.34 | 0.23 | 0.00 | 0.00 | 0.21 | 0.00 | 0.00 | 0.00 | 0.00 | 0.27 | - | 0.00 | 0.55 |
Insufficient Capacity | F13 | 0.47 | 0.69 | 0.00 | 0.39 | 0.00 | 0.15 | 0.35 | 0.00 | 0.00 | 0.38 | 0.00 | 0.00 | - | 0.29 |
Problems in Safety Issue | F14 | 0.00 | 0.00 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 0.27 | 0.00 | 0.00 | 0.34 | 0.00 | 0.00 | - |
In the modeling process, the structure of fuzzy cognitive map was analyzed using the FCM Expert software. The output of FCM static analysis, which is based on the principles of graph theory, was analyzed and the results are presented as degree of input, degree of output and centrality index of the BSC risk factors. Risk factors are ranked in Table, 3 based on the descending order of the centrality index. It should be noted that the higher the centrality index score of a factor is, the more influence it has on the network and plays a more central role in the fuzzy cognitive map.
Table 3. Ranking the risk factors of BSC
Factors | Indicator | Input | Output | Centrality |
|---|---|---|---|---|
Delays in Allocation and Distribution | F6 | 3.59 | 2.25 | 5.84 |
Disruptions in Logistics Processes | F7 | 2.98 | 2.55 | 5.53 |
Blood Shortages | F1 | 3.77 | 1.39 | 5.16 |
Weak Collaboration | F10 | 2.36 | 2.65 | 5.01 |
Insufficient Capacity | F13 | 1.95 | 2.72 | 4.67 |
Blood Wastage | F2 | 3.59 | 0.84 | 4.43 |
Demand and Supply Uncertainty | F4 | 1.07 | 3.06 | 4.13 |
Problems in Safety Issue | F14 | 3.12 | 1.01 | 4.13 |
Blood Products Perishability | F3 | 1.86 | 1.93 | 3.79 |
Infrastructure and Technological Limitations | F5 | 0 | 3.49 | 3.49 |
Problems in Quality Control | F11 | 0.88 | 1.73 | 2.61 |
Weak Screening | F12 | 0.75 | 1.6 | 2.35 |
Exceeding Blood Requisition in Actual Usage | F9 | 0.3 | 1.76 | 2.06 |
Complexities in Inventory Processes | F8 | 1.12 | 0.36 | 1.48 |
According to the results, "Delays in Allocation and Distribution" with the centrality score of 5.84 has the highest interaction with the BSC risk factors. Among the other risk factors, “Disruptions in Logistics Processes”, “Blood Shortages” and “Weak Collaboration” took the 2nd to 4th place, from the centrality point of view. The column related to the degree of output shows the total influence of each risk factor on other related risk factors; In this column, "Infrastructure and Technological Limitations", “Demand and Supply Uncertainty” and “Insufficient Capacity” with the output scores of 3.49, 3.06 and 2.72, respectively have the highest influences on the system’s risk factors. The input degree column also provides the total influence of the other risk factors on a given concept. Finally, "Blood Shortages", “Delays in Allocation and Distribution” and “Blood Wastage” with the input grade of 3.77, 3.59 and 3.59 has received the greatest influence from the system’s risk factors, respectively. Table 3 also provides other information of static analysis of fuzzy cognitive maps of the research.
Next, the FCM graphic structure of the BSC risk factors is presented in Fig. 2. In this fuzzy cognitive maps, the number of 14 risk factors are connected by 84 arcs that express the causal relationships between the related risk factors. The transfer function is considered “Sigmoid”, the activation rule is “Kosko’s activation rule with self-memory”, and the epsilon index (Convergence) is equal to 0.001.
Fig. 2. Graphical structure of the value chain drivers in pharmaceutical industry
In order to visually understand the FCM in Fig. 2, after eliminating the causal relationships with weights less than |±0.4|, the corresponding FCM was again presented in Fig. 3; So, only the most important causal relationships are displayed and a more accurate understanding of FCM is obtained for the viewer.
Fig. 3. Graphical structure with important causal relationships
Finally, model interface allows performing reasoning using the provided activation values. Before performing the inference process, the user must specify the activation values of input concepts used to activate the FCM-based system (see Editing concepts). This option will summarize the inference results through a chart and a table with the activation value of concepts at each iteration (see Fig. 4).
Fig. 4. The graphical interface results
The graphical interface visualizes the response vector obtained after adjusting the weights. It should be mentioned that the convergence index (ε) in this research considered 0.001.
CONCLUSIONS
This study has conducted an in-depth analysis of risk factors within the blood supply chain using Fuzzy Cognitive Mapping (FCM), providing crucial insights into the interconnected risks that can significantly impact the efficiency and reliability of blood supply operations. The centrality index derived from the FCM analysis has helped to identify and rank the most critical factors, highlighting key areas that demand immediate attention and strategic intervention. The findings revealed that "Delays in Allocation and Distribution" is the most central and influential risk factor in the blood supply chain, underscoring the critical need for optimizing distribution strategies and minimizing bottlenecks. Closely following this are "Disruptions in Logistics Processes" and "Blood Shortages," which further emphasize the necessity of a robust and agile logistics network that can adapt to varying demand levels and unforeseen disruptions. Other significant factors include "Weak Collaboration" and "Insufficient Capacity," both of which point to the need for stronger partnerships and improved resource management across the supply chain. "Blood Wastage" and "Demand and Supply Uncertainty" also emerged as critical risks, suggesting that better forecasting, inventory management, and technological integration could greatly reduce inefficiencies and wastage.
Given the identified risks, there is a clear need to develop more efficient and responsive allocation and distribution systems. This can be achieved through the adoption of advanced logistics technologies, such as real-time tracking systems and predictive analytics, to better manage distribution timelines and reduce the impact of potential disruptions. In addition, Improved collaboration among all stakeholders in the blood supply chain, including blood banks, hospitals, and transportation providers, is essential. Creating shared platforms for communication and data exchange can lead to more coordinated efforts and resource sharing, thereby enhancing overall capacity and responsiveness.
To tackle the issues of blood wastage and supply-demand imbalances, it is recommended to invest in more accurate demand forecasting tools and to optimize inventory management practices. This may include the implementation of machine learning algorithms for predictive modeling and the use of block chain technology for transparent tracking of blood products. Strengthening screening processes, adhering to stringent quality control standards, and adopting innovative preservation techniques for blood products can also help mitigate the risks associated with perishability and safety issues.
To further enhance the resilience and efficiency of blood supply chains, future studies could explore the integration of advanced machine learning models with FCM to develop more accurate predictive tools for managing demand fluctuations and optimizing inventory levels. Investigating the potential of block chain and the Internet of Things (IoT) for improving traceability, transparency, and real-time monitoring in the blood supply chain could provide new avenues for reducing risks and enhancing safety.
Research could also focus on developing comprehensive resilience models that consider the interdependencies between various risk factors, helping to create more robust blood supply chains that can withstand both internal and external shocks. Finally, future studies should examine the role of policy and regulatory frameworks in mitigating risks, particularly in areas such as collaboration, data sharing, and quality control, to ensure that best practices are consistently applied across all levels of the blood supply chain. By focusing on the suggested areas for future research, it is possible to develop more resilient, efficient, and safe blood supply chains that can better serve the needs of healthcare systems and improve patient outcomes globally.
REFERENCES
Aghsami, A., Abazari, S. R., Bakhshi, A., Yazdani, M. A., Jolai, S., & Jolai, F. (2023). A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems. Healthcare Analytics, 3, 100136.
Ala, A., Simic, V., Bacanin, N., & Babaee Tirkolaee, E. (2024). Blood supply chain network design with lateral freight: A robust possibilistic optimization model. Engineering Applications of Artificial Intelligence, 133(Part A), 108053.
Asadpour, M., Olsen, T. L., & Boyer, O. (2022). An updated review on blood supply chain quantitative models: A disaster perspective. Transportation Research Part E: Logistics and Transportation Review, 158, 102583.
Beliën, J., & Force, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.
Dehghani, M., Abbasi, B., & Oliveira, F. (2019). Proactive transshipment in the blood supply chain: A stochastic programming approach. Omega, 23, 1245-1253.
Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27–41.
Dutta, P., & Nagurney, N. (2019). Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers. Operations Research for Health Care, 23, 100230.
Entezari, S., Abdolazimi, O., Fakhrzad, M. B., Shishebori, D., & Ma, J. (2024). A bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics. Computers & Industrial Engineering, 188, 109899.
Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Hassanzadeh Amin, S. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236–250.
Ezugwu, A. E., Micheal, M. O., & Govender, P. (2019). Mathematical model formulation and hybrid metaheuristic optimization approach for near-optimal blood assignment in a blood bank system. Expert Systems with Applications, 137, 74-99.
Fahimnia, B., Jabbarzadeh, A., & Ghavamifar, A. (2018). Risk management in blood supply chains under emergency conditions. European Journal of Operational Research, 267(2), 1031-1047.
Fallahi, A., Mokhtari, H., & Akhavan Niaki, S. T. (2021). Designing a closed-loop blood supply chain network considering transportation flow and quality aspects. Sustainable Operations and Computers, 2, 170-189.
Fanoodi, M., Malmir, B., & Firouzi Jahantigh, F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in Biology and Medicine, 113, 103415.
Ghatreh Samani, M. R., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450–471.
Ghatreh Samani, M. R., Torabi, S. A., & Hosseini-Motlagh, S. M. (2018). Integrated blood supply chain planning for disaster relief. International Journal of Disaster Risk Reduction, 27, 168-188.
Ghorashi, S. B., Hamedi, M., & Sadeghian, R. (2020). Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computation & Applications, 32, 12173–12200.
Gilani Larimi, N., & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & Industrial Engineering, 137, 106014.
Gilani Larimi, N., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-Economic Planning Sciences, 68, 100697.
Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129-141.
Habibi-Kouchaksaraei, M., Paydar, M. M., & Asadi-Gangraj, E. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling, 55, 583-599.
Habibi-Kouchaksaraei, A., & Kazemi, A. (2020). Financial challenges in maintaining an efficient blood supply chain. Journal of Health Economics and Outcomes Research, 8(1), 29-37.
Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence, 90, 103493.
Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Cheraghi, S. (2019). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Faraji, M. (2024). Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty. Computers & Industrial Engineering, 188, 109908.
Jacobs, D. A., Silan, M. N., & Clemson, B. A. (1996). An analysis of alternative locations and service areas of American Red Cross blood facilities. Interfaces, 26(3), 40-50.
Kazemi, Z., Homayounfar, M., Fadaei, M., Soufi, M., & Salehzadeh, A. (2024). Multi-objective Optimization of Blood Supply Network Using the Meta-Heuristic Algorithms. Journal of Optimization in Industrial Engineering, (In Press), xx-xx.
Kenan, N., & Diabat, A. (2022). The supply chain of blood products in the wake of the COVID-19 pandemic: Appointment scheduling and other restrictions. Transportation Research Part E, 159, 102576.
Khalil, A., et al. (2020). Challenges in recruiting voluntary blood donors: A systematic review. Transfusion Medicine Reviews, 34(3), 173-179.K
Khalilpour Azari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Annals of Operations Research, 289, 123–152.
Kharaghani, M., Homayounfar, M., & Taleghani, M. (2023). Value Chains Analysis: Application of Fuzzy Cognitive Map in Pharmaceutical Industry. Journal of System Management, 10(2), 63-78.
Liu, X., & Song, X. (2019). Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-Papers OnLine, 52(13), 778-783.
Mansur, A., Handayani, D. I., Wangsa, I. D., Utama, D. M., & Jauhari, W. A. (2023). A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types. Decision Analytics Journal, 8, 100279.
Meneses, M., Santos, D., & Barbosa-Póvoa, A. (2023). Modelling the blood supply chain. European Journal of Operational Research, 307(2), 499-518.
Movahedi, M., Homayounfar, M., Fadaei Eshkiki, M., & Soufi, M. (2022). Ranking Tehran Stock Exchange Industries using a Combined FCM-ELECTRE III-LA Method. Iranian Journal of Optimization, 14(3), 173-188.
Nezamoddini, N., Gholami, A., & Aqlan, F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks. International Journal of Production Economics, 225, 107569.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2018). Whole blood or apheresis donations? A multi-objective stochastic optimization approach. European Journal of Operational Research, 266(1), 193-204.
Pirabán, A., Guerrero, W. J., & Labadie, N. (2019). Survey on blood supply chain management: Models and methods. Computers and Operations Research, 112, 104756.
Rajendran, S., & Ravindran, S. R. (2019). Inventory management of platelets along blood supply chain to minimize wastage and shortage. Computers & Industrial Engineering, 130, 714–730.
Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2019). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research, 283, 679–703.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283, 1413–1462.
Setiawati, M., Simatupang, T. M., & Okdinawati, L. (2024). A dynamics approach for maturity assessment of hospital supply chain management. Journal of Optimization in Industrial Engineering, 17(1), 87-102.
Sharifi, M., & Ghaneipour, M. (2019). Technological challenges in Iran's blood supply chain. Iranian Journal of Blood and Cancer, 11(2), 79-85.
Shih, H., Kasaie, A., & Rajendran, S. (2023). A multiple criteria decision-making model for minimizing platelet shortage and outdating in blood supply chains under demand uncertainty. Healthcare Analytics, 3, 100180.
Suen, T. Y., Song, X., & Jones, D. (2023). A two-stage stochastic model for a multi-objective blood platelet supply chain network design problem incorporating frozen platelets. Computers & Industrial Engineering, 185, 109651.
Tavakol, P., Nahavandi, B., & Homayounfar, M. (2023). A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in COVID-19 pandemic. Journal of Optimization in Industrial Engineering, 34, 105–118.
Torrado, A., & Barbosa-Póvoa, A. (2022). Towards an optimized and sustainable blood supply chain network under uncertainty: A literature review. Cleaner Logistics and Supply Chain, 3, 100028.
Toude Bahambari, R., & Soufi, M. (2019). Investigating the Moderating Role of Competitive Strategies on the Impact of Supply Chain Integration on the Financial and Operational Performance (Case Study: The car manufacturing industry in IRAN. Iranian Journal of Optimization, 11(2), 137-147.
Wang, C., & Chen, S. (2020). A distributionally robust optimization for blood supply network considering disasters. Transportation Research Part E: Logistics and Transportation Review, 134, 101840.
Wang, K. M., & Ma, Z. J. (2015). Age-based policy for blood transshipment during blood shortage. Transportation Research Part E: Logistics and Transportation Review, 80, 166-183.
World Health Organization (2017). Global status report on blood safety and availability 2016. WHO Press.
