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.
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Acknowledgements
We would like to express our appreciation for the Iran National Science Foundation (INSF) for their financial support. We would also like to show our gratitude to the Editor and reviewers for the time and expertise devoted to reviewing our paper as well as their insightful suggestions and comments.
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Nasrollahi, M., Razmi, J. A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty. Oper Res Int J 21, 525–552 (2021). https://doi.org/10.1007/s12351-019-00459-3
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DOI: https://doi.org/10.1007/s12351-019-00459-3