IDEAS home Printed from https://ideas.repec.org/a/spr/operea/v21y2021i1d10.1007_s12351-019-00459-3.html
   My bibliography  Save this article

A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty

Author

Listed:
  • Meisam Nasrollahi

    (University of Tehran)

  • Jafar Razmi

    (University of Tehran)

Abstract

This study presents an extension of the integrated Pharmaceutical Supply Chain design with maximum expected coverage, where different hospitals with specific reliability value for different pharmaceutical substances are considered. A four-layer multi-period supply chain including manufacturers, Distribution Centers, hospitals and patients is considered in this research. To increase the supply chain reliability, the pharmaceutical substance flow in each individual layer is allowed. Pharmaceutical substance’s priority for each hospital varies according to its patients types. A reliability index for pharmaceutical substances is proposed to consider substances priority and improve the service level of hospitals for patients. A multi-objective model that increases demands’ coverage and minimizes the total costs is developed. Centroid method is applied to handle demand uncertainty and a Multi-objective Non-dominated Ranked Genetic Algorithm is proposed to solve the model. This algorithm can generate a Pareto front with higher quality and better diversity when compared with Multiple Objective Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm II. As a case study, the proposed model is applied for the medical sector in the Fars province of Iran. Results show that when different reliability level for various pharmaceutical substances in different hospitals is considered, significantly better results in terms of unsatisfied demand without a significant increase in costs are obtained. In addition, the proposed approach is able to provide solutions to improve the supply chain reliability in a practical pharmaceutical supply network.

Suggested Citation

  • Meisam Nasrollahi & Jafar Razmi, 2021. "A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty," Operational Research, Springer, vol. 21(1), pages 525-552, March.
    • Handle: RePEc:spr:operea:v:21:y:2021:i:1:d:10.1007_s12351-019-00459-3
    DOI: 10.1007/s12351-019-00459-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12351-019-00459-3
    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/s12351-019-00459-3?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. Teye, Collins & Bell, Michael G H & Bliemer, Michiel C J, 2017. "Urban intermodal terminals: The entropy maximising facility location problem," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 64-81.
    2. Ozbaygin, Gizem & Ekin Karasan, Oya & Savelsbergh, Martin & Yaman, Hande, 2017. "A branch-and-price algorithm for the vehicle routing problem with roaming delivery locations," Transportation Research Part B: Methodological, Elsevier, vol. 100(C), pages 115-137.
    3. Sharon J Williams, 2017. "Improving Healthcare Operations," Springer Books, Springer, number 978-3-319-46913-3, September.
    4. Rezvani, S., 2015. "Ranking generalized exponential trapezoidal fuzzy numbers based on variance," Applied Mathematics and Computation, Elsevier, vol. 262(C), pages 191-198.
    5. Evers, Philip T., 2001. "Heuristics for assessing emergency transshipments," European Journal of Operational Research, Elsevier, vol. 129(2), pages 311-316, March.
    6. Asandului, Laura & Roman, Monica & Fatulescu, Puiu, 2013. "The Efficiency of Healthcare Systems in Europe: a Data Envelopment Analysis Approach," MPRA Paper 58954, University Library of Munich, Germany, revised Apr 2014.
    7. Sorensen, Paul & Church, Richard, 2010. "Integrating expected coverage and local reliability for emergency medical services location problems," Socio-Economic Planning Sciences, Elsevier, vol. 44(1), pages 8-18, March.
    8. Dongyan Chen & Chan He & Senlin Wu, 2016. "Single facility collection depots location problem with random weights," Operational Research, Springer, vol. 16(2), pages 287-299, July.
    9. Tsai, I-Chun, 2017. "The source of global stock market risk: A viewpoint of economic policy uncertainty," Economic Modelling, Elsevier, vol. 60(C), pages 122-131.
    10. Yue Zhang & Martin Puterman, 2013. "Developing an adaptive policy for long-term care capacity planning," Health Care Management Science, Springer, vol. 16(3), pages 271-279, September.
    11. Marek Gebicki & Ed Mooney & Shi-Jie Chen & Lukasz Mazur, 2014. "Evaluation of hospital medication inventory policies," Health Care Management Science, Springer, vol. 17(3), pages 215-229, September.
    12. Miroslav Marić & Zorica Stanimirović & Srdjan Božović, 2015. "Hybrid metaheuristic method for determining locations for long-term health care facilities," Annals of Operations Research, Springer, vol. 227(1), pages 3-23, April.
    13. Nazaré Rego & João Claro & Jorge Pinho de Sousa, 2014. "A hybrid approach for integrated healthcare cooperative purchasing and supply chain configuration," Health Care Management Science, Springer, vol. 17(4), pages 303-320, December.
    14. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
    15. Zahiri, Behzad & Zhuang, Jun & Mohammadi, Mehrdad, 2017. "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 109-142.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fatemi, Maedeh S. & Ghodratnama, Ali & Tavakkoli-Moghaddam, Reza & Kaboli, Amin, 2022. "A multi-functional tri-objective mathematical model for the pharmaceutical supply chain considering congestion of drugs in factories," Research in Transportation Economics, Elsevier, vol. 92(C).
    2. Ieva Meidute-Kavaliauskiene & Figen Yıldırım & Shahryar Ghorbani & Renata Činčikaitė, 2022. "The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    3. Munyaradzi Bvuchete & Sara Saartjie Grobbelaar & Joubert van Eeden, 2021. "A Network Maturity Mapping Tool for Demand-Driven Supply Chain Management: A Case for the Public Healthcare Sector," Sustainability, MDPI, vol. 13(21), pages 1-29, October.

    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. Das, Debojyoti & Kannadhasan, M. & Bhattacharyya, Malay, 2019. "Do the emerging stock markets react to international economic policy uncertainty, geopolitical risk and financial stress alike?," The North American Journal of Economics and Finance, Elsevier, vol. 48(C), pages 1-19.
    2. Binh Nguyen Quang & Thai‐Ha Le & Canh Nguyen Phuc, 2022. "Influences of uncertainty on the returns and liquidity of cryptocurrencies: Evidence from a portfolio approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2497-2513, April.
    3. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    4. Chen, Xu & Li, Shanshan & Wang, Xiaojun, 2020. "Evaluating the effects of quality regulations on the pharmaceutical supply chain," International Journal of Production Economics, Elsevier, vol. 230(C).
    5. Livio Di Matteo & Thomas Barbiero, 2020. "Spend Less, Get More? Explaining Health Spending and Outcome Differences Between Canada and Italy," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 12(4), pages 403-438, December.
    6. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    7. Sinha, Priyank & Kumar, Sameer & Chandra, Charu, 2023. "Strategies for ensuring required service level for COVID-19 herd immunity in Indian vaccine supply chain," European Journal of Operational Research, Elsevier, vol. 304(1), pages 339-352.
    8. Jiang, Xiaodan & Fan, Houming & Luo, Meifeng & Xu, Zhenlin, 2020. "Strategic port competition in multimodal network development considering shippers’ choice," Transport Policy, Elsevier, vol. 90(C), pages 68-89.
    9. Elliot Lee & Mariel Lavieri & Michael Volk & Yongcai Xu, 2015. "Applying reinforcement learning techniques to detect hepatocellular carcinoma under limited screening capacity," Health Care Management Science, Springer, vol. 18(3), pages 363-375, September.
    10. Clavijo-Buritica, Nicolás & Triana-Sanchez, Laura & Escobar, John Willmer, 2023. "A hybrid modeling approach for resilient agri-supply network design in emerging countries: Colombian coffee supply chain," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    11. Yao, Can-Zhong & Liu, Cheng & Ju, Wei-Jia, 2020. "Multifractal analysis of the WTI crude oil market, US stock market and EPU," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    12. Laura Calvet & Rocio de la Torre & Anita Goyal & Mage Marmol & Angel A. Juan, 2020. "Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review," Administrative Sciences, MDPI, vol. 10(3), pages 1-23, July.
    13. Dumez, Dorian & Lehuédé, Fabien & Péton, Olivier, 2021. "A large neighborhood search approach to the vehicle routing problem with delivery options," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 103-132.
    14. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
    15. Chen, Xiaoyu & Chiang, Thomas C., 2020. "Empirical investigation of changes in policy uncertainty on stock returns—Evidence from China’s market," Research in International Business and Finance, Elsevier, vol. 53(C).
    16. Luis Francisco López-Castro & Elyn L. Solano-Charris, 2021. "Integrating Resilience and Sustainability Criteria in the Supply Chain Network Design. A Systematic Literature Review," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    17. Ranajit Kumar Bairagi, 2022. "Dynamic Impacts of Economic Policy Uncertainty on Australian Stock Market: An Intercontinental Evidence," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 21(1), pages 64-91, March.
    18. Chang, Kuang-Liang, 2022. "Do economic policy uncertainty indices matter in joint volatility cycles between U.S. and Japanese stock markets?," Finance Research Letters, Elsevier, vol. 47(PA).
    19. Hochmuth, Christian A. & Köchel, Peter, 2012. "How to order and transship in multi-location inventory systems: The simulation optimization approach," International Journal of Production Economics, Elsevier, vol. 140(2), pages 646-654.
    20. Shao, Ying-Hui & Yang, Yan-Hong & Zhou, Wei-Xing, 2022. "How does economic policy uncertainty comove with stock markets: New evidence from symmetric thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(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:operea:v:21:y:2021:i:1:d:10.1007_s12351-019-00459-3. 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.