IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v328y2023i1d10.1007_s10479-022-04829-7.html
   My bibliography  Save this article

A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty

Author

Listed:
  • Alireza Goli

    (University of Isfahan)

  • Ali Ala

    (Shanghai Jiao Tong University)

  • Seyedali Mirjalili

    (Torrens University Australia, Fortitude Valley, Brisbane
    Yonsei University)

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.

Suggested Citation

  • Alireza Goli & Ali Ala & Seyedali Mirjalili, 2023. "A robust possibilistic programming framework for designing an organ transplant supply chain under uncertainty," Annals of Operations Research, Springer, vol. 328(1), pages 493-530, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-04829-7
    DOI: 10.1007/s10479-022-04829-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04829-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-04829-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gilani, H. & Sahebi, H. & Oliveira, Fabricio, 2020. "Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model," Applied Energy, Elsevier, vol. 278(C).
    2. Caruso, Valeria & Daniele, Patrizia, 2018. "A network model for minimizing the total organ transplant costs," European Journal of Operational Research, Elsevier, vol. 266(2), pages 652-662.
    3. Nan Kong & Andrew J. Schaefer & Brady Hunsaker & Mark S. Roberts, 2010. "Maximizing the Efficiency of the U.S. Liver Allocation System Through Region Design," Management Science, INFORMS, vol. 56(12), pages 2111-2122, December.
    4. Mahdi Yousefi Nejad Attari & Ali Ebadi Torkayesh & Behnam Malmir & Ensiyeh Neyshabouri Jami, 2021. "Robust possibilistic programming for joint order batching and picker routing problem in warehouse management," International Journal of Production Research, Taylor & Francis Journals, vol. 59(14), pages 4434-4452, July.
    5. Mustafa Akan & Oguzhan Alagoz & Baris Ata & Fatih Safa Erenay & Adnan Said, 2012. "A Broader View of Designing the Liver Allocation System," Operations Research, INFORMS, vol. 60(4), pages 757-770, August.
    6. Jimenez, Mariano & Arenas, Mar & Bilbao, Amelia & Rodri'guez, M. Victoria, 2007. "Linear programming with fuzzy parameters: An interactive method resolution," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1599-1609, March.
    7. Dubois, Didier & Fargier, Helene & Fortemps, Philippe, 2003. "Fuzzy scheduling: Modelling flexible constraints vs. coping with incomplete knowledge," European Journal of Operational Research, Elsevier, vol. 147(2), pages 231-252, June.
    8. Maria Bruni & Domenico Conforti & Nicola Sicilia & Sandro Trotta, 2006. "A new organ transplantation location–allocation policy: a case study of Italy," Health Care Management Science, Springer, vol. 9(2), pages 125-142, May.
    9. Sinem Savaşer & Ömer Burak Kınay & Bahar Yetis Kara & Pelin Cay, 2019. "Organ transplantation logistics: a case for Turkey," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 327-356, June.
    10. Seyed Mahdi Aghazadeh & Mohammad Mohammadi & Bahman Naderi, 2018. "Robust bi-objective cost-effective, multi-period, location-allocation organ transplant supply chain," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 29(1), pages 17-36.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kargar, Bahareh & Pishvaee, Mir Saman & Jahani, Hamed & Sheu, Jiuh-Biing, 2020. "Organ transportation and allocation problem under medical uncertainty: A real case study of liver transplantation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
    2. Baris Ata & Yichuan Ding & Stefanos Zenios, 2021. "An Achievable-Region-Based Approach for Kidney Allocation Policy Design with Endogenous Patient Choice," Manufacturing & Service Operations Management, INFORMS, vol. 23(1), pages 36-54, 1-2.
    3. Tsao, Yu-Chung & Thanh, Vo-Van, 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, Elsevier, vol. 124(C), pages 13-39.
    4. Peidro, David & Mula, Josefa & Jiménez, Mariano & del Mar Botella, Ma, 2010. "A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment," European Journal of Operational Research, Elsevier, vol. 205(1), pages 65-80, August.
    5. Guihua Wang & Ronghuo Zheng & Tinglong Dai, 2022. "Does Transportation Mean Transplantation? Impact of New Airline Routes on Sharing of Cadaveric Kidneys," Management Science, INFORMS, vol. 68(5), pages 3660-3679, May.
    6. Han-Lin Li & Yao-Huei Huang & Shu-Cherng Fang, 2017. "Linear Reformulation of Polynomial Discrete Programming for Fast Computation," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 108-122, February.
    7. Sinem Savaşer & Ömer Burak Kınay & Bahar Yetis Kara & Pelin Cay, 2019. "Organ transplantation logistics: a case for Turkey," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(2), pages 327-356, June.
    8. Alumura, Sibel A. & Karab, Bahar Y. & Melo, M. Teresa, 2013. "Location and logistics," Technical Reports on Logistics of the Saarland Business School 5, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    9. Farhad Hasankhani & Amin Khademi, 2021. "Is it Time to Include Post‐Transplant Survival in Heart Transplantation Allocation Rules?," Production and Operations Management, Production and Operations Management Society, vol. 30(8), pages 2653-2671, August.
    10. Misra, Akansha & Saranga, Haritha & Tripathi, Rajeev R, 2022. "Channel choice and incentives in the cadaveric organ supply chain," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1202-1214.
    11. Sepehr Nemati & Zeynep G. Icten & Lisa M. Maillart & Andrew J. Schaefer, 2020. "Mitigating Information Asymmetry in Liver Allocation," INFORMS Journal on Computing, INFORMS, vol. 32(2), pages 234-248, April.
    12. Ozge Ceren Ersoy & Diwakar Gupta & Timothy Pruett, 2021. "A critical look at the U.S. deceased‐donor organ procurement and utilization system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 3-29, February.
    13. Tinglong Dai & Ronghuo Zheng & Katia Sycara, 2020. "Jumping the Line, Charitably: Analysis and Remedy of Donor-Priority Rule," Management Science, INFORMS, vol. 66(2), pages 622-641, February.
    14. Chaithanya Bandi & Nikolaos Trichakis & Phebe Vayanos, 2019. "Robust Multiclass Queuing Theory for Wait Time Estimation in Resource Allocation Systems," Management Science, INFORMS, vol. 65(1), pages 152-187, January.
    15. 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, Elsevier, vol. 72(C), pages 101-124.
    16. Caruso, Valeria & Daniele, Patrizia, 2018. "A network model for minimizing the total organ transplant costs," European Journal of Operational Research, Elsevier, vol. 266(2), pages 652-662.
    17. Dhirendra Prajapati & M. Manoj Kumar & Saurabh Pratap & H. Chelladurai & Mohd Zuhair, 2021. "Sustainable Logistics Network Design for Delivery Operations with Time Horizons in B2B E-Commerce Platform," Logistics, MDPI, vol. 5(3), pages 1-13, September.
    18. Li, Mengling & Riyanto, Yohanes E. & Xu, Menghan, 2023. "Prioritized organ allocation rules under compatibility constraints," Games and Economic Behavior, Elsevier, vol. 141(C), pages 403-427.
    19. Olcay Polat & Duygu Topaloğlu, 2022. "Collection of different types of milk with multi-tank tankers under uncertainty: a real case study," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-33, April.
    20. Kwon, Oseok & Han, Jeehoon, 2021. "Waste-to-bioethanol supply chain network: A deterministic model," Applied Energy, Elsevier, vol. 300(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-04829-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.