Journal of Industrial Engineering and Management Studies

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Indexing and Abstracting

Related Links

FAQ

Double-Blind Peer Review

News

Metrics

Design of a scenario-based multi-level and multi-objective mathematical model with the aim of reducing the risk of the blood supply chain in the conditions of the COVID-19 pandemic

Document Type : Original Article

Authors

1 Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Management, Nowshahr Branch, Islamic Azad University, Nowshahr, Iran

3 Depattment of Industrial Management, west Teharan Branch, Islamic Azad University, Tehran Iran

Abstract

Supply chain risk management is a preventive approach to risk management in the supply chain to avoid possible unex-pected consequences and to manage the blood supply chain (BSC) and achieve the maximum effectiveness and efficiency of this chain, risk management of the BSC is inevitable. This research aims to propose a mathematical model to reduce the risk of the BSC in the conditions of the COVID-19 pandemic. One of the most important contributions of this research is to consider the uncertainty in the demand parameter in the conditions of the COVID-19 pandemic and to provide a ro-bust planning model to overcome it in order to properly manage and control its risks. For this purpose, in this research a scenario-based multi-objective model is proposed with the aim of reducing the risk of the BSC in the conditions of the COVID-19 pandemic. In order to test the model, the problem is investigated in different sizes and using actual data and the results are presented, and sensitivity analysis is carried out on the changes in the parameters. Baron solver in GAMS 24.9 software is used to solve the proposed mathematical model. The proposed model determines the product sent from the blood center to the hospital, the amount of product produced in the blood center, the amount of blood collected from donors, the number of collection centers, the amount of blood stock in the blood center and hospital with the aim of reduc-ing cost and risk and increasing reliability. In this research, a scenario-based non-linear integer multi-objective model is proposed considering the level of supply and with the aim of reducing the risk of the BSC by reducing the cost and in-creasing the reliability of the BSC in the conditions of the COVID-19 pandemic, which can be used for risk management of the BSC in critical conditions of blood supply, such as the COVID-19 pandemic. Finally, to measure the sensitivity of the presented model performance to the change in the parameters, the sensitivity analysis on the behavior of the model in terms of the change in the shortage cost, the number of blood collection facilities and the objective functions is presented. The sensitivity analysis on the shortage cost parameter showed that with the increase in the shortage cost, the shortage rate decreased and this leads to an increase in the total cost.

Keywords

References
Erlansari, A., Effendi, R., Wijanarko, A., Susilo, B., & Hardiansyah, R. (2021). Backpropagation and fuzzy algorithm Modelling to Resolve Blood Supply Chain Issues in the Covid-19 Pandemic. arXiv preprint arXiv:2109.02645.
Cagliano, A. C., Grimaldi, S., Rafele, C., & Campanale, C. (2022). An enhanced framework for blood supply chain risk management. Sustainable Futures, 4, 100091.
Ramírez, A. P., & Labadie, N. (2017, October). Stochastic inventory control and distribution of blood products. In Proceedings of the International Conference on Industrial Engineering and Operations Management Bogota, Colombia.
Najafi, M., Ahmadi, A., & Zolfagharinia, H. (2017). Blood inventory management in hospitals: Considering supply and demand uncertainty and blood transshipment possibility. Operations Research for Health Care, 15, 43-56.
Jabbarzadeh, A., Oghyani, M., & Sadjadi, J. (2015). Provide a robust optimization model for designing the blood supply chain network in crisis situations with regard to reliability. Quality Engineering and Management Journal, 5(2), 85-96.
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
Ekici, A., Özener, O. Ö., & Çoban, E. (2018). Blood supply chain management and future research opportunities. Operations research applications in health care management, 241-266.
Asgharizadeh, E., Kadivar, M., Noroozi, M., Mottaghi, V., Mohammadi, H., & Chobar, A. P. (2022). The intelligent traffic management system for emergency medical service station location and allocation of ambulances. Computational intelligence and neuroscience, 2022.
Babaeinesami, A., Ghasemi, P., Chobar, A. P., Sasouli, M. R., & Lajevardi, M. (2022). A New Wooden Supply Chain Model for Inventory Management Considering Environmental Pollution: A Genetic algorithm. Foundations of Computing and Decision Sciences, 47(4), 383-408.
Whitaker, B. I., Green, J., King, M. R., Leibeg, L. L., Mathew, S. M., Schlumpf, K. S., & Schreiber, G. B. (2008). The 2007 national blood collection and utilization survey. Rockville, Maryland: The United States Department of Health and Human Services.
Sverdrup, K., Kimmerle, S. J., & Berg, P. (2017). Computational investigation of the stability and dissolution of nanobubbles. Applied Mathematical Modelling, 49, 199-219.
Darvish Motevali, M. H., & Motamedi, M. (2020). Dynamic modeling to evaluate the efficiency of a sequential multilevel supply network. Journal of Decisions and Operations Research, 5(3), 272-289. doi: 10.22105/dmor.2020.242474.1196
Davoudi-Kiakalayeh, A., Paridar, M., & Toogeh, G. (2012). Cost unit analysis of blood transfusion centers in Guilan province. Scientific Journal of Iran Blood Transfus Organ, 9(3), 346-352.
Daneshvar, A., Radfar, R., Ghasemi, P., Bayanati, M., & Pourghader Chobar, A. (2023). Design of an optimal robust possibilistic model in the distribution chain network of agricultural products with high perishability under uncertainty. Sustainability, 15(15), 11669.
Duan, Q., & Liao, T. W. (2014). Optimization of blood supply chain with shortened shelf lives and ABO compatibility. International journal of production economics, 153, 113-129.
 Ensafian, H., Yaghoubi, S., & Yazdi, M. M. (2017). Raising quality and safety of platelet transfusion services in a patient-based integrated supply chain under uncertainty. Computers & Chemical Engineering, 106, 355-372.
Emami, A., Hazrati, R., Delshad, M. M., Pouri, K., Khasraghi, A. S., & Chobar, A. P. (2023). A novel mathematical model for emergency transfer point and facility location. Journal of Engineering Research.
Farrokhizadeh, E., Seyfi-Shishavan, S. A., & Satoglu, S. I. (2022). Blood supply planning during natural disasters under uncertainty: a novel bi-objective model and an application for red crescent. Annals of Operations Research, 319(1), 73-113.
Ghahremani-Nahr, J., Nozari, H., & Bathaee, M. (2021). Robust box Approach for blood supply chain network design under uncertainty: hybrid moth-flame optimization and genetic algorithm. International Journal of Innovation in Engineering, 1(2), 40-62.
Pandey, H. C., Coshic, P., CS, C., Arcot, P. J., & Kumar, K. (2021). Blood supply management in times of SARS‐CoV‐2 pandemic–challenges, strategies adopted, and the lessons learned from the experience of a hospital‐based blood centre. Vox Sanguinis, 116(5), 497-503.
Shirazi, H., Kia, R., & Ghasemi, P. (2021). A stochastic bi-objective simulation–optimization model for plasma supply chain in case of COVID-19 outbreak. Applied Soft Computing, 112, 107725.
Hosseini, S., Ahmadi Choukolaei, H., Ghasemi, P., Dardaei-beiragh, H., Sherafatianfini, S., & Pourghader Chobar, A. (2022). Evaluating the performance of emergency centers during coronavirus epidemic using multi-criteria decision-making methods (case study: sari city). Discrete Dynamics in Nature and Society, 2022.
Ensafian, H., & Yaghoubi, S. (2017). Robust optimization model for integrated procurement, production and distribution in platelet supply chain. Transportation Research Part E: Logistics and Transportation Review, 103, 32-55.
Kohneh, J. N., Teymoury, E., & Pishvaee, M. S. (2016). Blood products supply chain design considering disaster circumstances (Case study: earthquake disaster in Tehran). Journal of Industrial and Systems Engineering, 9(special issue on supply chain), 51-72.
Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 43(1), 1-19.
Motamedi, M., Movahedi, M. M., Rezaian Zaidi, J., & Rashidi Komijan, A. (2019). Designing a Non-Linear Mixed Integer Two-objective Math Model to Maximize the Reliability of Blood Supply Chain. Journal of Quality Engineering and Management, 8(4), 259-274.
Movahedi, M. M., & Rezaian Zaidi, J. (2020). Factors Affecting Blood Donation in the Blood Supply Chain Under Critical Conditions. Journal of Police Medicine, 9(2), 71-78.
Yousefi Nejad Attari, M., Pasandide, S. H. R., Agaie, A., & Akhavan Niaki, S. T. (2017). Presenting a stochastic multi choice goal programming model for reducing wastages and shortages of blood products at hospitals. Journal of Industrial and Systems Engineering, 10(special issue on healthcare), 81-96.
Samani, M. R. G., 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.
Shokouhifar, M., Sabbaghi, M. M., & Pilevari, N. (2021). Inventory management in blood supply chain considering fuzzy supply/demand uncertainties and lateral transshipment. Transfusion and Apheresis Science, 60(3), 103103.
Arani, M., Chan, Y., Liu, X., & Momenitabar, M. (2021). A lateral resupply blood supply chain network design under uncertainties. Applied mathematical modelling, 93, 165-187.
Maashisani, F., Hajiaghaei–Keshteli, M., Gholipour-Kanani, Y., & Harsej, F. (2022). Optimization of the Blood Supply Chain Network with the Possibility of Lateral Delivery. Journal of Operational Research In Its Applications (Applied Mathematics)-Lahijan Azad University, 19(3), 63-88.
Shokouhifar, M., & Ranjbarimesan, M. (2022). Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic. Cleaner Logistics and Supply Chain, 5, 100078.
Nagurney, A., Masoumi, A. H., & Yu, M. (2012). Supply chain network operations management of a blood banking system with cost and risk minimization. Computational management science, 9, 205-231.
Azezan, N. A., Ramli, M. F., & Masran, H. (2017, November). A review on the modelling of collection and distribution of blood donation based on vehicle routing problem. In AIP Conference Proceedings (Vol. 1905, No. 1, p. 040008). AIP Publishing LLC.
Clay, N. M., Abbasi, B., Eberhard, A., & Hearne, J. (2018). On the volatility of blood inventories. International Transactions in Operational Research, 25(1), 215-24.
Privett, N., & Gonsalvez, D. (2014). The top ten global health supply chain issues: perspectives from the field. Operations Research for Health Care, 3(4), 226-230.
Omidkhoda, A., Amini Kafi-Abad, S., Pourfathollah, A. A., & Maghsudlu, M. (2017). Management of production, processing, dispensing and monitoring of blood components in Iran (2009–2015). Scientific Journal of Iran Blood Transfus Organ, 14(3), 155-163.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2015). A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making. International Journal of Production Research, 53(24), 7191-7212.
Ghasemi, P., Goodarzian, F., Abraham, A., & Khanchehzarrin, S. (2022). A possibilistic-robust-fuzzy programming model for designing a game theory-based blood supply chain network. Applied Mathematical Modelling, 112, 282-303.
Uthayakumar, S. Priyan, Pharmaceutical supply chain and inventory management strategies: Optimization for a pharmaceutical company and a hospital, Oper. Res. Health Care 2 (2013) 52–64.
Ramezanian, R., & Behboodi, Z. (2017). Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation Research Part E: Logistics and Transportation Review, 104, 69-82.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2021). A robust framework for designing blood network in disaster relief: a real-life case. Operational Research, 21, 1529-1568.
Sibevei, A., Azar, A., & Zandieh, M. (2022). Developing a Two-stage Robust Stochastic Model for Designing a Resilient Blood Supply Chain Considering Earthquake Disturbances and Infectious Diseases. Industrial Management Journal, 13(4), 664-703.
Seyfi-Shishavan, S. A., Donyatalab, Y., Farrokhizadeh, E., & Satoglu, S. I. (2021). A fuzzy optimization model for designing an efficient blood supply chain network under uncertainty and disruption. Annals of operations research, 1-55.
Mouatassim, S., Ahlaqqach, M., Benhra, J., & Eloualidi, M. (2016). Model based on hybridized game theory to optimize logistics case of blood supply chain. International Journal of Computer Applications, 145(15), 15.
Moslemi, S., & Mirzazadeh, A. (2017). Performance evaluation of four-stage blood supply chain with feedback variables using NDEA cross-efficiency and entropy measures under IER uncertainty. Numerical Algebra, Control and Optimization, 7(4), 379-401.
Soltani, M., Mohammadi, R. A., Hosseini, S. M. H., & Zanjani, M. M. (2021). A new model for blood supply chain network design in disasters based on hub location approach considering intercity transportation. International Journal of Industrial Engineering & Production Research, 32(2), 1-28.
Cheraghi, S., & Hosseini-Motlagh, S. M. (2017). Optimal blood transportation in disaster relief considering facility disruption and route reliability under uncertainty. International Journal of Transportation Engineering, 4(3), 225-254.
Zahiri, B., Torabi, S. A., Mousazadeh, M., & Mansouri, S. A. (2015). Blood collection management: Methodology and application. Applied Mathematical Modelling, 39(23-24), 7680-7696.
Zendehdel, M., Bozorgi-Amiri, A., & Omrani, H. (2014). A location Model for Blood Donation Camps with Consideration of Disruption. Advances in Industrial Engineering, 48(Special Issue), 33-43.
Journal of Industrial Engineering and Management Studies
Volume 10, Issue 2
December 2023
Pages 59-74
Files
Share
How to cite
Statistics