Development of Multi - Objective, Multi- Period and Multi - Level Blood Supply Chain Planning Model
Subject Areas :
Health Management Services
Fatemeh Maashisani
1
,
Mostafa Hajiaghaei-Keshteli
2
,
Yousef Gholipour- kanani
3
,
Fatemeh Harsej
4
1 - Phd Student of Industrial Engineering Department, Nour Branch, Islamic Azad University, Nour, Iran
2 - Assistant Professor, Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
3 - Assistant Professor, Department of Industrial Engineering, Qaemshahr Branch, Islamic Azad University, Qemshahr, Iran
4 - Assistant Professor, Department of Industrial Engineering, Nour Branch, Islamic Azad University, Nour, Iran
Received: 2021-06-28
Accepted : 2021-10-28
Published : 2021-08-23
Keywords:
Blood supply chain network,
Uncertainty,
lateral delivery,
Hospital,
Abstract :
Introduction: Lack of proper planning in blood supply may lead to irreparable damage of humans. The purpose of this study is to determine the optimal plan for donating, storing and sending blood to hospitals in each period to minimize the cost of set up and design a blood supply chain and its delivery time.Methods: This study is applied in terms of purpose and descriptive-analytical methodology. The model was solved with the uncertainty approach using the Epsilon constraint method in GAMZ software. To evaluate the accuracy of the model, a case study was conducted in 5 regions of Mazandaran province and by performing sensitivity analysis on key parameters, its effect on total cost and time of blood transfusion cycle was investigated.Results: The results of this study indicate the high accuracy of the model with the possibility of lateral delivery between hospitals. With a 5% reduction in transport time to 15%, a reduction in blood cycle time and a 25% reduction in this time has 26% reduction in the total blood transfusion process.Conclusion: Lateral blood delivery between hospitals was used as a solution to increase the model's ability to respond hospital's demand and also reduce shortages costs and lack of blood in the blood supply chain. Also, the important parameters of the problem such as blood supply, demand, capacity of each center and related costs in the blood supply chain and the possibility of lateral delivery of blood products were analyzed to validate the model.
References:
Samani MR, Hosseini-Motlagh SM, Ghannadpour SF. A multilateral perspective towards blood network design in an uncertain environment. Methodology and implementation. Comput Ind Eng, 2019; 130: 450-471.
Boonyanusith W, Jittamaip. Blood supply chain risk management using house of risk model. Walailak J Sci Technol, 2019; 16(8): 573-91.
Mansouri E, Hajiaghaiee-Keshteli M, Tavakkoli-Moghaddam R. Development of a Forward/Reverse Logistic Network in Health Care under Uncertainty and Disaster. Journal of Emergency Management, 2017; 6(1): 5-17.
Ibrahim IN, Mamman AI, Balogun MS, Abubakar A, Awwalu S, Kusfa IU, Usman AB, Waziri AD, Muktar HM. Motivation for donation among hospital blood donors and their attitude towards voluntary blood donation in State Government Hospitals, Kaduna, Nigeria. ISBT Science Series, 2019; 14(4): 345-51.
Managing Director of Iranian blood transfusion organization: 2.1 million blood units are donated annually in the country. Mehr News Agency, December 17; 2016.
Ghorashi SB, Hamedi M, Sadeghian R. Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computing and Applications, 2020; 32(16): 12173-12200.
Sibevei A, Azar A, Zandieh M. Using a two-step approach of risk matrix and DEMATEL to identify and analyze the most important risks in the blood supply chain. Journal of Healthcare Management, 2020; 11(2): 7-20.
Sureshchannder GS, Rajendran C, Anantharaman RN. The relationship between service quality andcustomer satisfactions -a factor specific approach. Journal of Service Marketing, 2003; 16(4): 363-379.
Osorio A. F, Brailsford S. C and Smith H. K. A structured review of quantitative models in the blood supply chain. A taxonomic framework for decision-making: International Journal of Production Research, 2015; 53(24): 7191-7212.
Rahmani D. Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research, 2019; 283(1): 613-641.
Dagne TB, Jayaprkash J, & Geremew Gebeyehu S. Design of Supply Chain Network Model for Perishable Products with Stochastic Demand: An Optimized Model. Journal of Optimization in Industrial Engineering, 2020; 13(1): 29-37.
Hsu CN, Lu PC, Hou CY, Tain YL. Blood pressure abnormalities associated with gut microbiota-derived short chain fatty acids in children with congenital anomalies of the kidney and urinary tract. Journal of clinical medicine, 2019; 8(8): 1090.
Nagurney, A., Masoumi, A., H.Yu, M. Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 2012; 9(2): 205-231.
Jahani, M., Eskandari, F., Mahmoudjanloo, S., Mahmoudi, G. The Causes of the Mortality of Inpatients in the hospitals covered by Semnan Province Universities of Medical Sciences Based on ICD10. Journal of healthcare management, 2017; 8(3): 7-16.
Goldfarb RS. Shortage, Shortage, Who's Got the Shortage? The Journal of Economic Education, 2013; 44(3): 277-97.
Rezaie N, Maarefdoust Z, Amini Kafiabad S, Mahdizadeh M, Birjandi F. Evaluation of the blood usage and wastage in Kerman hospitals: Sci J Iran Blood Transfus Organ, 2013; 10(3): 213-221.
Nahmias S. Heidelberg: Perishable Inventory Theory. Springer; 2011.
Mousavi R, Salehi-Amiri A, Zahedi A, Hajiaghaei-Keshteli M. Designing a supply chain network for blood decomposition by utilizing social and environmental factor. Computers & Industrial Engineering, 2021; 160: 107501.
Arvan, M., Tavakkoli-Moghaddam, R. and Abdollahi, M. Designing a biobjective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management, 2015; 3(1): 57-68.
Hamdan B, Diabat A. A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 2019; 101: 130-43.
Dehghani M, Abbasi B. An age-based lateral-transshipment policy for perishable items. International Journal of Production Economics, 2018; 198: 93-103.
Hosseini-Motlagh S.-M., M.R.G. Samani, & Cheraghi S. Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 2020; 70: 100725.
Hosseini-Motlagh SM, Samani MR, Homaei S. Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 2020; 11(3): 1085-104.
Derikvand H, et al., A robust stochastic bi objective model for blood inventory-distribution management in a blood supply chain. European Journal of Industrial Engineering, 2020; 14(3): 369-403.
Doodman M, & Bozorgi Amiri A. Integrate Blood Supply Chain Network Design with Considering Lateral Transshipment under Uncertainty. Journal of Industrial Management Perspective, 2020; 9(4): 9-40.
Shander A, Hofmann A, Gombotz H, Theusinger OM, Spahn DR. Estimating the cost of blood: past, present, and future directions. Best Practice & Research Clinical Anaesthesiology, 2007; 21(2): 271-289.
Chen H, Chiang RH, Storey VC, Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 2012; 4(36): 1165-1188.
Silva Filho OS., Carvalho M. A., Cezarino W, Silva R, & Salviano G. Demand forecasting for blood components distribution of a blood supply chain. IFAC Proceedings Volumes, 2012; 46(24): 565-571.
Far RM, Rad FS, Abdolazimi Z, Kohan MM. Determination of rate and causes of wastage of blood and blood products in Iranian hospitals. Turkish Journal of Hematology, 2014 31(2): 161.
Alahyari, M., Pilevari, N., Radfar, R. Providing a Model for Assessing Pharmaceutical Industries Supply Chain Sustainability Using Adaptive Neuro- Fuzzy Inference System (ANFIS). Journal of healthcare management, 2019; 10(3): 77-88.
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Samani MR, Hosseini-Motlagh SM, Ghannadpour SF. A multilateral perspective towards blood network design in an uncertain environment. Methodology and implementation. Comput Ind Eng, 2019; 130: 450-471.
Boonyanusith W, Jittamaip. Blood supply chain risk management using house of risk model. Walailak J Sci Technol, 2019; 16(8): 573-91.
Mansouri E, Hajiaghaiee-Keshteli M, Tavakkoli-Moghaddam R. Development of a Forward/Reverse Logistic Network in Health Care under Uncertainty and Disaster. Journal of Emergency Management, 2017; 6(1): 5-17.
Ibrahim IN, Mamman AI, Balogun MS, Abubakar A, Awwalu S, Kusfa IU, Usman AB, Waziri AD, Muktar HM. Motivation for donation among hospital blood donors and their attitude towards voluntary blood donation in State Government Hospitals, Kaduna, Nigeria. ISBT Science Series, 2019; 14(4): 345-51.
Managing Director of Iranian blood transfusion organization: 2.1 million blood units are donated annually in the country. Mehr News Agency, December 17; 2016.
Ghorashi SB, Hamedi M, Sadeghian R. Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computing and Applications, 2020; 32(16): 12173-12200.
Sibevei A, Azar A, Zandieh M. Using a two-step approach of risk matrix and DEMATEL to identify and analyze the most important risks in the blood supply chain. Journal of Healthcare Management, 2020; 11(2): 7-20.
Sureshchannder GS, Rajendran C, Anantharaman RN. The relationship between service quality andcustomer satisfactions -a factor specific approach. Journal of Service Marketing, 2003; 16(4): 363-379.
Osorio A. F, Brailsford S. C and Smith H. K. A structured review of quantitative models in the blood supply chain. A taxonomic framework for decision-making: International Journal of Production Research, 2015; 53(24): 7191-7212.
Rahmani D. Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research, 2019; 283(1): 613-641.
Dagne TB, Jayaprkash J, & Geremew Gebeyehu S. Design of Supply Chain Network Model for Perishable Products with Stochastic Demand: An Optimized Model. Journal of Optimization in Industrial Engineering, 2020; 13(1): 29-37.
Hsu CN, Lu PC, Hou CY, Tain YL. Blood pressure abnormalities associated with gut microbiota-derived short chain fatty acids in children with congenital anomalies of the kidney and urinary tract. Journal of clinical medicine, 2019; 8(8): 1090.
Nagurney, A., Masoumi, A., H.Yu, M. Supply chain network operations management of a blood banking system with cost and risk minimization. Computational Management Science, 2012; 9(2): 205-231.
Jahani, M., Eskandari, F., Mahmoudjanloo, S., Mahmoudi, G. The Causes of the Mortality of Inpatients in the hospitals covered by Semnan Province Universities of Medical Sciences Based on ICD10. Journal of healthcare management, 2017; 8(3): 7-16.
Goldfarb RS. Shortage, Shortage, Who's Got the Shortage? The Journal of Economic Education, 2013; 44(3): 277-97.
Rezaie N, Maarefdoust Z, Amini Kafiabad S, Mahdizadeh M, Birjandi F. Evaluation of the blood usage and wastage in Kerman hospitals: Sci J Iran Blood Transfus Organ, 2013; 10(3): 213-221.
Nahmias S. Heidelberg: Perishable Inventory Theory. Springer; 2011.
Mousavi R, Salehi-Amiri A, Zahedi A, Hajiaghaei-Keshteli M. Designing a supply chain network for blood decomposition by utilizing social and environmental factor. Computers & Industrial Engineering, 2021; 160: 107501.
Arvan, M., Tavakkoli-Moghaddam, R. and Abdollahi, M. Designing a biobjective and multi-product supply chain network for the supply of blood. Uncertain Supply Chain Management, 2015; 3(1): 57-68.
Hamdan B, Diabat A. A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 2019; 101: 130-43.
Dehghani M, Abbasi B. An age-based lateral-transshipment policy for perishable items. International Journal of Production Economics, 2018; 198: 93-103.
Hosseini-Motlagh S.-M., M.R.G. Samani, & Cheraghi S. Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 2020; 70: 100725.
Hosseini-Motlagh SM, Samani MR, Homaei S. Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 2020; 11(3): 1085-104.
Derikvand H, et al., A robust stochastic bi objective model for blood inventory-distribution management in a blood supply chain. European Journal of Industrial Engineering, 2020; 14(3): 369-403.
Doodman M, & Bozorgi Amiri A. Integrate Blood Supply Chain Network Design with Considering Lateral Transshipment under Uncertainty. Journal of Industrial Management Perspective, 2020; 9(4): 9-40.
Shander A, Hofmann A, Gombotz H, Theusinger OM, Spahn DR. Estimating the cost of blood: past, present, and future directions. Best Practice & Research Clinical Anaesthesiology, 2007; 21(2): 271-289.
Chen H, Chiang RH, Storey VC, Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 2012; 4(36): 1165-1188.
Silva Filho OS., Carvalho M. A., Cezarino W, Silva R, & Salviano G. Demand forecasting for blood components distribution of a blood supply chain. IFAC Proceedings Volumes, 2012; 46(24): 565-571.
Far RM, Rad FS, Abdolazimi Z, Kohan MM. Determination of rate and causes of wastage of blood and blood products in Iranian hospitals. Turkish Journal of Hematology, 2014 31(2): 161.
Alahyari, M., Pilevari, N., Radfar, R. Providing a Model for Assessing Pharmaceutical Industries Supply Chain Sustainability Using Adaptive Neuro- Fuzzy Inference System (ANFIS). Journal of healthcare management, 2019; 10(3): 77-88.
