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

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  • 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
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    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.

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