Multi-Objective Optimization of Blood Products Supply Network to Minimize Delivery Time and Non-Estimated Hospital Demand

Subject Areas : Health Management Services

Zeinab  Kazemi ¹ (PhD Candidate, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mahdi  Homayounfar ² (Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
mehdi  fadaei ³ (Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Mansour  Soufi ⁴ (Assistant Professor, Department of Industrial Management, Rasht Branch, Islamic Azad University, Rasht, Iran)
Ali  salehzadeh ⁵ (Assistant Professor, Department of Biology, Rasht Branch, Islamic Azad University, Rasht, Iran)

Received: 2022-05-30 Accepted : 2022-07-11 Published : 2022-02-20

Keywords: Blood product supply network, Meta-Heuristic Algorithm, hybrid approach, Multi-objective optimization,

Abstract :

Introduction: Due to the importance of blood as a vital element in the health system, in this study, the blood supply chain is modeled at three levels of donors, banks (blood centers) and hospitals in the form of a multi-objective model to minimize total costs, total delivery time of blood units and non-estimated demand of hospitals in each period.Methods: The present study is applied in terms of purpose and descriptive and quantitative in terms of method. The data needed to implement the real problem in 2021 have been collected by through the regional office of the Tehran blood transfusion organization along with the Negareh system. Due to the Np-hard nature of the problem, the proposed model is solved using three algorithms of GA, NSGA-II and MOPSO in GAMS software.Results: In the proposed model, matching the blood type in meeting demand; blood type delivery and allocation system in laboratories and blood banks, blood wasting in laboratory, transfer of products between demand centers, sensitive and determinative parameters of the model such as; demand, blood donation and delivery time of blood products between network components are considered indefinitely. The findings show that the MOPSO algorithm has a better performance in problems 3, 7, 10 and 12 for the QM index, but generally, based on running times and their average, the NSGA-II algorithm is better.Conclusion: Based on the results, the proposed model leads to a reduction in total costs, total delivery time of blood units and unapproved demand of hospitals.

References:

1- Manavizadeh N, Mashayekhi N, Shabani M. Designing a fuzzy green blood supply chain network with regard to reducing blood product waste, Second International Conference on Challenges and New Solutions in Industrial Engineering, Management and Accounting, Damghan, 2021. [In Persian]

2- Zahiri B, Pishvaee MS. Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research, 2017; 55(7): 2013-2033.

3- Dehghani M, Abbasi B. An age-based lateral-transshipment policy for perishable items. International Journal of Production Economics, 2019; 198: 93-103.

4- Cheraghi S, Hoseini Motlagh S, Ghatreh Samani M. A robust bi-objective model for integrated blood supply chain network design considering transshipment between facilities under uncertainty. Quarterly Journal of Transportation Engineering, 2019; 10(4): 737-770. [In Persian]

5- Tofighi S, Torabi SA, Mansouri SA. Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 2016; 250(1): 239-250.

6- Mousavi R, Salehi-Amiri AH, Zahedia A, Hajiaghaei-Keshteli M. Designing a supply chain network for blood decomposition by utilizing social and environmental factor. Computers & Industrial Engineering, 2021; 160: 107501.

7- Kazemi Matin R, Azadi M, Farzipoor-Saen R. Measuring the sustainability and resilience of blood supply chains, Decision Support Systems, 2021; 21356765.

8- Shokouhifar M, Sabbaghi MM, Pilevari N. Inventory management in blood supply chain considering fuzzy supply/ demand uncertainties and lateral transshipment. Transfusion and Apheresis Science, 2021; 60: 103103

9- Civelek I, Karaesmen I, Scheller-Wolf A. Blood platelet inventory management with protection levels. European Journal of Operational Research, 2015; 243(3): 826-838.

10- 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. [In Persian]

11- Eskandari-Khanghahi M, Tavakkoli-Moghaddam R, Taleizadeh AA, Amin SH. Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 2018; 71: 236-250.

12- Dehghani M, Abbasi B, Oliveira F. Proactive transshipment in the blood supply chain: A stochastic programming approach. Omega, 2019; 23: 1245-1253.

13- Bohonek M, Kutac D, Acker JP, Seghatchian J. (2020). Optimizing the supply of whole blood-derived bioproducts through the combined implementation of cryopreservation and pathogen reduction technologies and practices: an overview. Transfus Apher Sci: 102754.

14- Ahmadimanesh M, Tavakoli A, Pooya A, Dehghanian F. Designing an optimal inventory management model for the blood supply chain. Quality Improvement Study, 2020; 1-8. [In Persian]

15- Zhou Y, Zou T, Liu C, Yu H, Chen L, Su J. Blood supply chain operation considering lifetime and transshipment under uncertain environment. Applied Soft Computing, 2021; 106: 107364.

16- Arani M, Chan Y, Liu X, Momenitabar M. A lateral resupply blood supply chain network design under uncertainties. Applied Mathematical Modelling, 2021; 93: 165–187.

17- Maashisani F, Hajiaghaei-Keshteli M, Gholipour- kanani Y, Harsej F. Development of Multi - Objective, Multi- Period and Multi- Level Blood Supply Chain Planning Model. Journal of healthcare management, 2021; 12(2): 71-85. [In Persian]

18- Arvan M, Tavakkoli-Moghaddam R, Abdollahi M. Designing a bi-objective, multi-product supply chain network for blood supply. Uncertain Supply Chain Management, 2015; 3: 57-68.

19- Gunpinar S, Centeno L. Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 2015; 54: 129-141.