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Design and Optimization of a Resilient and Responsive Pharmaceutical Supply Chain Considering Uncertainty and Perishability

Bahmanabadi, Arefeh | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58437 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Rezapour Niari, Maryam
  7. Abstract:
  8. The pharmaceutical industry, as one of the most vital pillars of the healthcare system, plays a key role in improving quality of life. Rising global demand, coupled with threats such as natural disasters, pandemics, and shortages of raw materials, has highlighted the necessity of redesigning pharmaceutical supply chains with a resilience-oriented approach. This study proposes a multi-objective mathematical model for designing a pharmaceutical supply chain network that enhances system efficiency by minimizing costs, unmet demand, and delivery time. The model also accounts for drug perishability and defines disposal centers for the safe elimination of expired medicines. To address demand uncertainty, two approaches—stochastic programming and scenario-based robust optimization—are compared. The model is solved using an extended ε-constraint method, yielding a set of Pareto-optimal solutions. Results indicate that while the stochastic model achieves a lower average shortage, it lacks stability under critical scenarios. In contrast, the scenario-based robust model, by reducing variability, provides higher reliability and better performance under crisis conditions. Sensitivity analysis reveals that rising demand is the most significant threat to service level, and reducing shortages requires identifying bottlenecks and strengthening network capacity. Evaluations of strategies such as contracting with backup suppliers, increasing production capacity, utilizing safety stock, employing diverse transportation modes, and adopting multi-sourcing confirm their effectiveness in reducing shortages and enhancing resilience. Furthermore, lateral transshipment among distribution centers was found to decrease delivery time. The proposed model can serve as a practical tool for healthcare decision-makers to improve supply chain recovery capability, mitigate losses caused by crises, and enhance the effectiveness of pharmaceutical distribution
  9. Keywords:
  10. Resilient Supply Chain ; Stochastic Programming ; Scenario-Based Robustness Model ; Deterioration ; Responsive Supply Chain ; Pharmaceutical Supply Chain ; Uncertainty

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