Abstract
Organ transplantation is a crucial task in the healthcare supply chain, which organizes the supply and demand for various vital organs. In this regard, dealing with uncertainty is one of the main challengings in designing an organ transplant supply chain. To address this gap, in the present research, a mathematical formulation and solution method is proposed to optimize the organ transplants supply chain under shipment time uncertainty. A possibilistic programming model and simulation-based solution method are developed for organ transplant center location, allocation, and distribution. The proposed mathematical model optimizes the overall cost by considering the fuzzy uncertainty of organ demands and transportation time. Moreover, a novel simulation-based optimization is applied using the credibility theory to deal with the uncertainty in the optimization of this mathematical model. In addition, the proposed model and solution method are evaluated by implementing different test problems. The numerical results demonstrate that the optimal credibility level is between 0.2 and 0.6 in all tested cases. Moreover, the patient’s satisfaction rate is higher than the viability rate in the designed organ supply chain.
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Aghazadeh, S. M., Mohammadi, M., & Naderi, B. (2018). Robust bi-objective cost-effective, multi-period, location-allocation organ transplant supply chain. International Journal of Logistics Systems and Management, 29(1), 17–36.
Akan, M., Alagoz, O., Ata, B., Erenay, F. S., & Said, A. (2012). A broader view of designing the liver allocation system. Operations Research, 60, 757–770.
Alagoz, O., Schaefer, A.J. & Roberts, M.S., (2009). Optimizing organ allocation and acceptance. In:Handbook of optimization in medicine. (pp. 1–24), Springer.
Behroozi, F., Monfared, M. A. S., & Hosseini, S. M. H. (2021). Investigating the conflicts between different stakeholders’ preferences in a blood supply chain at emergencies: a trade-off between six objectives. Soft Computing, 25, 13389–13410.
Beliën, J., De Boeck, L., Colpaert, J., Devesse, S., & Van den Bossche, F. (2013). Optimizing the facility location design of organ transplant centers. Decision Support Systems, 54(4), 1568–1579.
Bruni, M. E., Conforti, D., Sicilia, N., & Trotta, S. (2006). A new organ transplantation location–allocation policy: A case study of Italy. Health Care Management Science, 9, 125–142.
Caruso, V., & Daniele, P. (2018). A network model for minimizing the total organ transplant costs. European Journal of Operational Research, 266(2), 652–662.
Dubois, D., Fargier, H., & Fortemps, P. (2003). Fuzzy scheduling: Modelling flexible constraints vs. co** with incomplete knowledge. European Journal of Operational Research, 147(2), 231–252.
Ghaderi, H., Moini, A., & Pishvaee, M. S. (2018). A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design. Journal of Cleaner Production, 179, 368–406.
Ghandforoush, P., & Sen, T. K. (2010). A DSS to manage platelet production supply chain for regional blood centers. Decision Support Systems, 50(1), 32–42.
Gilani, H., Sahebi, H., & Oliveira, F. (2020). Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model. Applied Energy, 278, 115653.
Goli, A., & Alinaghian, M. (2015). Location and multi-depot vehicle routing for emergency vehicles using tour coverage and random sampling. Decision Science Letters, 4(4), 579–592.
Günay, E. E., Kremer, G. E. O., & Zarindast, A. (2021). A multi-objective robust possibilistic programming approach to sustainable public transportation network design. Fuzzy Sets and Systems, 422, 106–129.
Habib, M. S., Asghar, O., Hussain, A., Imran, M., Mughal, M. P., & Sarkar, B. (2021). A robust possibilistic programming approach toward animal fat-based biodiesel supply chain network design under uncertain environment. Journal of Cleaner Production, 278, 122403.
Hashemi Doulabi, H., & Khalilpourazari, S. (2022). Stochastic weekly operating room planning with an exponential number of scenarios. Annals of Operations Research, 1–22.
Van den Hout, W. B., Smits, J. M., Deng, M. C., Hummel, M., Schoendube, F., Scheld, H. H., Persijn, G. G., & Laufer. (2003). The heart-allocation simulation model: A tool for comparison of transplantation allocation policies1. Transplantation, 76(10), 1492–1497.
Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: A brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy Sets and Systems, 111(1), 3–28.
Jiménez, M., Arenas, M., Bilbao, A., & Rodrı, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599–1609.
Kargar, B., Pishvaee, M. S., Jahani, H., & Sheu, J. B. (2020). Organ transportation and allocation problem under medical uncertainty: A real case study of liver transplantation. Transportation Research Part e: Logistics and Transportation Review, 134, 101841.
Khalilpourazari, S., & Hashemi Doulabi, H. (2021). Robust modelling and prediction of the COVID-19 pandemic in Canada. International Journal of Production Research, 1–17.
Khalilpourazari, S., & Hashemi Doulabi, H. (2022). A flexible robust model for blood supply chain network design problem. Annals of operations research, 1–26.
Khoshsirat, M., Dabbagh, R., & Bozorgi-Amiri, A. (2021). A multi-objective robust possibilistic programming approach to coordinating procurement operations in the disaster supply chain using a multi-attribute reverse auction mechanism. Computers & Industrial Engineering, 158, 107414.
Kong, N., Schaefer, A. J., Hunsaker, B., & Roberts, M. S. (2010). Maximizing the efficiency of the US liver allocation system through region design. Management Science, 56, 2111–2122.
Oztekin, A., Kong, Z. J., & Delen, D. (2011). Development of a structural equation modeling-based decision tree methodology for the analysis of lung transplantations. Decision Support Systems, 51(1), 155–166.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy Sets and Systems, 206, 1–20.
Rouhani, S., Pishvaee, M., & Zarrinpoor, N. (2021). A fuzzy optimization approach to strategic organ transplantation network design problem: A real case study. Decision Science Letters, 10(3), 195–216.
Sabouhi, F., Tavakoli, Z. S., Bozorgi-Amiri, A., & Sheu, J. B. (2019). A robust possibilistic programming multi-objective model for locating transfer points and shelters in disaster relief. Transportmetrica a: Transport Science, 15(2), 326–353.
Savaşer, S., Kınay, Ö. B., Kara, B. Y., & Cay, P. (2019). Organ transplantation logistics: A case for Turkey. Or Spectrum, 41(2), 327–356.
Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems Engineering Procedia, 5, 21–28.
Shaverdi, M., Yaghoubi, S., & Ensafian, H. (2020). A multi-objective robust possibilistic model for technology portfolio optimization considering social impact and different types of financing. Applied Soft Computing, 86, 105892.
Taranto, S. E., Harper, A. M., Edwards, E. B., Rosendale, J. D., McBride, M. A., Daily, O. P., ... & Schmeiser, B. (2000). Develo** a national allocation model for cadaveric kidneys. In 2000 Winter, Simulation Conference Proceedings (Cat. No. 00CH37165) (Vol. 2, pp. 1971–1977). IEEE.
Thompson, D., Waisanen, L., Wolfe, R., Merion, R. M., McCullough, K., & Rodgers, A. (2004). Simulating the allocation of organs for transplantation. Health Care Management Science, 7(4), 331–338.
Tsao, Y. C., & Thanh, V. V. (2019). A multi-objective mixed robust possibilistic flexible programming approach for sustainable seaport-dry port network design under an uncertain environment. Transportation Research Part e: Logistics and Transportation Review, 124, 13–39.
Uehlinger, N., Beyeler, F., Marti, H. P., & Immer, F. F. (2010). Organ transplantation in Switzerland: Impact of the new transplant law on cold ischaemia time and organ transports. Swiss Medical Weekly, 140, 222.
Wen, M., & Kang, R. (2011). Some optimal models for facility location–allocation problem with random fuzzy demands. Applied Soft Computing, 11(1), 1202–1207.
Yousefi Nejad Attari, M., Ebadi Torkayesh, A., Malmir, B., & Neyshabouri Jami, E. (2021). Robust possibilistic programming for joint order batching and picker routing problem in warehouse management. International Journal of Production Research, 59(14), 4434–4452.
Zadeh, L. A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1(1), 3–28.
Zahiri, B., Tavakkoli-Moghaddam, R., Mohammadi, M., & Jula, P. (2014). Multi-objective design of an organ transplant network under uncertainty. Transportation Research Part e: Logistics and Transportation Review, 72, 101–124.
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Goli, A., Ala, A. & Mirjalili, S. A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty. Ann Oper Res 328, 493–530 (2023). https://doi.org/10.1007/s10479-022-04829-7
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DOI: https://doi.org/10.1007/s10479-022-04829-7