Document Type : Research Paper
Authors
Department of Industrial Engineering, Payame Noor University, Tehran, Iran.
Abstract
The growing need for adequate and safe blood and the high costs of health systems have prompted governments to improve the functioning of health systems. One of the most critical parts of a health system is the blood supply chain, which accounts for a significant share of the health system's costs. In the present study, with an operational approach, the total network costs are minimized along with the minimization of transportation time and lead time of delivery of blood products. Also, determining the optimal routing decisions is improved the level of responsiveness and reliability of the network. In this research, a multi-objective stochastic nonlinear mixed-integer model has been developed for Tehran's blood supply chain network. Robust scenario-based programming is capable of effectively controlling parametric uncertainty and the level of risk aversion of network decisions. Also, the proposed reliability approach controls the adverse effects of disturbances and creates an adequate confidence level in the capacity of the network blood bank. Lastly, the model is solved through the Lagrangian relaxation algorithm. Comparison of the results shows the high convergence rate of the solutions in the Lagrangian relaxation algorithm.
Keywords
Main Subjects
- Kuruppu, K. K. (2010). Management of blood system in disasters. Biologicals, 38(1), 87-90. https://doi.org/10.1016/j.biologicals.2009.10.005
- Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European journal of operational research, 217(1), 1-16.
- 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. https://doi.org/10.1007/s10479-021-04123-y
- 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. https://doi.org/10.1016/j.apm.2020.12.010
- Shen, Z. J. M., Zhan, R. L., & Zhang, J. (2011). The reliable facility location problem: formulations, heuristics, and approximation algorithms. INFORMS journal on computing, 23(3), 470-482. https://doi.org/10.1287/ijoc.1100.0414
- Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International journal of production economics, 212, 125-138. https://doi.org/10.1016/j.ijpe.2018.09.018
- Kaveh, A., & Ghobadi, M. (2017). A multistage algorithm for blood banking supply chain allocation problem. International journal of civil engineering, 15(1), 103-112. https://doi.org/10.1007/s40999-016-0032-3
- Arvan, M., Tavakkoli-Moghaddam, R., & Abdollahi, M. (2015). Designing a bi-objective and multi-product supply chain network for the supply of blood. Uncertain supply chain management, 3(1), 57-68.
- Chaiwuttisak, P., Smith, H., Wu, Y., Potts, C., Sakuldamrongpanich, T., & Pathomsiri, S. (2016). Location of low-cost blood collection and distribution centres in Thailand. Operations research for health care, 9, 7-15. https://doi.org/10.1016/j.orhc.2016.02.001
- 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. https://doi.org/10.1016/j.tre.2017.06.004
- Zahiri, B., Torabi, S. A., Mohammadi, M., & Aghabegloo, M. (2018). A multi-stage stochastic programming approach for blood supply chain planning. Computers & industrial engineering, 122, 1-14. https://doi.org/10.1016/j.cie.2018.05.041
- 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 computing and applications, 32(16), 12173-12200.
- Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers & operations research, 89, 206-212. https://doi.org/10.1016/j.cor.2016.08.014
- Dutta, P., & Nagurney, A. (2019). Multitiered blood supply chain network competition: linking blood service organizations, hospitals, and payers. Operations research for health care, 23, 100230. https://doi.org/10.1016/j.orhc.2019.100230
- 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. https://doi.org/10.1016/j.engappai.2020.103493
- Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems engineering procedia, 5, 21-28. https://doi.org/10.1016/j.sepro.2012.04.004
- Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transportation research part E: logistics and transportation review, 70, 225-244. https://doi.org/10.1016/j.tre.2014.06.003
- 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.
- Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International journal of production economics, 183, 700-709. https://doi.org/10.1016/j.ijpe.2015.11.007
- Zahiri, B., & Pishvaee, M. S. (2017). Blood supply chain network design considering blood group compatibility under uncertainty. International journal of production research, 55(7), 2013-2033. https://doi.org/10.1080/00207543.2016.1262563
- 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(1), 679-703.
- Cheraghi, S., Hoseini-Motlagh, M., & ghatreh-Samani, M. (2019). Providing a robust two-objective model for integrated design of blood supply chain network under conditions of demand uncertainty and the possibility of lateral delivery between facilities. Quarterly journal of transportation engineering, 10(4), 737-770. (In Persian). http://jte.sinaweb.net/article_63214_4ea7e95225a56
pdf - Rahmani, D. (2019). Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of operations research, 283(1), 613-641. https://doi.org/10.1007/s10479-018-2960-6
- 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. https://doi.org/10.1016/j.tre.2019.08.005
- Dehghani, M., Abbasi, B., & Oliveira, F. (2021). Proactive transshipment in the blood supply chain: a stochastic programming approach. Omega, 98, 102112. https://doi.org/10.1016/j.omega.2019.102112
- 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 and computers, 2, 170-189. https://doi.org/10.1016/j.susoc.2021.07.002
- Hamidieh, A., & Arshadikhamseh, A. (2021). The flexible possibilistic-robust mathematical programming approach for the resilient supply chain network: An operational plan. Journal of advanced manufacturing systems, 20(03), 473-498. https://doi.org/10.1142/S0219686721500220
- Fazli-Khalaf, M., Naderi, B., Mohammadi, M., & Pishvaee, M. S. (2020). Design of a sustainable and reliable hydrogen supply chain network under mixed uncertainties: a case study. International journal of hydrogen energy, 45(59), 34503-34531. https://doi.org/10.1016/j.ijhydene.2020.05.276
- Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465. https://doi.org/10.1016/j.amc.2009.03.037
- Mulvey, J. M., & Ruszczyński, A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations research, 43(3), 477-490. https://doi.org/10.1287/opre.43.3.477
- Held, M., & Karp, R. M. (1970). The traveling-salesman problem and minimum spanning trees. Operations research, 18(6), 1138-1162. https://doi.org/10.1287/opre.18.6.1138
- Held, M., & Karp, R. M. (1971). The traveling-salesman problem and minimum spanning trees: Part II. Mathematical programming, 1(1), 6-25. https://doi.org/10.1007/BF01584070
- Diabat, A., Battaïa, O., & Nazzal, D. (2015). An improved Lagrangian relaxation-based heuristic for a joint location-inventory problem. Computers & operations research, 61, 170-178. https://doi.org/10.1016/j.cor.2014.03.006