Robust Multi-Objective Optimization Model for the Integration of Blood Production and Distribution Planning

Shearly Christina Tanjung(1*), Eric Wibisono(2), Dina Natalia Prayogo(3),

(1) University of Surabaya
(2) University of Surabaya
(3) University of Surabaya
(*) Corresponding Author
DOI: https://doi.org/10.23917/jiti.v22i1.20115

Abstract

Blood is a very important element for humans. Currently, The Blood Transfusion Unit of The Indonesian Red Cross in City “X” determines the amount and type of blood to be processed based on stock availability. This method tends to be subjective so that the possibility of error in production decision is fairly high. This research intends to manage that the blood processing process and allocation can be carried out optimally and on target. The research objectives are to minimize the number of blood shortages, expired blood, and the total costs incurred, by applying a robust optimization method that considers the uncertainty of blood demand and the disturbances in the blood production process. Pass data of demands will be used for forecasting demand in the planning period. The forecast results can be adjusted to current conditions using the adjustment ratios. The robust optimization method can produce decisions that tend to be stable even when there are changes in blood demand. The results obtained in this study were 24% decrease in the number of shortages of blood stock, 88% decrease in the amount of expired blood, and 96% decrease in the overproduction cost.

Keywords

blood production and distribution; multi-objective; pre-emptive goal programming; robust optimization

Full Text:

PDF

References

Beliën, J. & Forcé, H. (2012). “Supply chain management of blood products: A literature review,” European Journal of Operational Research, 217: 1–16. DOI: https://doi.org/10.1016/j.ejor.2011.05.026.

Bertsimas, D. & Thiele, A. (2006). “A Robust Optimization Approach to Inventory Theory,” Operations Research 54(1), 150–168. DOI: https://doi.org/10.1287/opre.1050.0238.

Boonyanusith, W. & Jittamai, P. (2019). “Blood supply chain risk management using house of risk model,” Walailak J Sci & Tech, 16(8): 573–591. DOI: https://doi.org/10.48048/wjst.2019.3472.

Cagliano, A. C., Grimaldi, S. & Rafele, C. (2021). “A structured approach to analyse logistics risks in the blood transfusion process,” Journal of Healthcare Risk Management, 41(2): 18–30. DOI: https://doi.org/10.1002/jhrm.21458.

Fallahi, A., Mokhtari, H. & Niaki, S. T. A. (2021). “Designing a closed-loop blood supply chain network considering transportation flow and quality aspects,” Sustainable Operations & Computers, 2: 170–189. DOI: https://doi.org/10.1016/j.susoc.2021.07.002.

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. DOI: https://doi.org/10.1016/j.apm.2022.08.003.

Hamadneh, S., Pedersen, O., Alshurideh, M., Kurdi, B. A. & Alzoubi, H. M. (2021). “An investigation of the role of supply chain visibility into the Scottish blood supply chain,” Journal of Legal, Ethical and Regulatory Issue, 24(Special Issue 1): 1–12.

Hosseini-Motlagh, S.-M., Samani, M. R. G. & Homaei, S. (2020). “Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real‑life case),” Journal of Ambient Intelligence and Humanized Computing, 11, 1085–1104. DOI: https://doi.org/10.1007/s12652-019-01315-0.

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 E, 159: 102576. DOI: https://doi.org/10.1016/j.tre.2021.102576.

Khalilpourazari, S. & Doulabi, H. H. (2022). “A flexible robust model for blood supply chain network design problem,” Annals of Operations Research, Springer. DOI: https://doi.org/10.1007/s10479-022-04673-9.

Liu, W., Ke, G. Y., Chen, J. & Zhang, L. (2020). “Scheduling the distribution of blood products: A vendor-managed inventory routing approach,” Transportation Research Part E, 140: 101964. DOI: https://doi.org/10.1016/j.tre.2020.101964.

Meidute-Kavaliauskiene, I., Yazdi, A. K. & Mehdiabadi, A. (2022). “Integration of Blockchain Technology and Prioritization of Deployment Barriers in the Blood Supply Chain,” Logistics, 6(21): 1–16. DOI: https://doi.org/10.3390/logistics6010021.

Mishra, S., Daga, A. & Gupta, A. (2021). “Inventory management practices in the blood bank of an institute of national importance in India,” Journal of Family Medicine and Primary Care, 10(12): 4489–4492. DOI: https://doi.org/10.4103/jfmpc.jfmpc_1000_21.

Ramezanian, R. & Behboodi, Z. (2017). “Blood supply chain network design under uncertainties in supply and demand considering social aspects,” Transportation Research E, 104: 69–82. DOI: https://doi.org/10.1016/j.tre.2017.06.004.

Razavi, N., Gholizadeh, H., Nayeri, S. & Ashrafi, T. A. (2020). “A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics,” Journal of the Operational Research Society, 72(12): 2804–2828. DOI: https://doi.org/10.1080/01605682.2020.1821586.

Article Metrics

Abstract view(s): 125 time(s)
PDF: 110 time(s)

Refbacks

  • There are currently no refbacks.