System Engineering and Productivity

System Engineering and Productivity

Robust Optimization of Pharmaceutical Logistics Network under Disaster Considering Reliability

Articles in Press

Document Type : Research Paper

Authors
Hamid Moakedi 1
Fatemeh Ghasemizadeh 2
1 Corresponding author: Assistant Professor, Department of Industrial Engineering, College of Engineering, Qom University of Technology, Qom, Iran
2 M.Sc., Department of Industrial Engineering, College of Farabi, University of Tehran, Tehran, Iran
10.22034/sep.2026.2087772.1482
Abstract
n this paper, an integer linear programming model is proposed for designing a pharmaceutical logistics network in two phases of disaster preparedness and response. In the preparedness phase, strategic decisions including location of central, local, and backup warehouses, capacity determination, perishable drug inventory management, and fleet allocation are made. In the response phase, temporary warehouses are activated considering the probability of route failures, and routing and distribution are performed dynamically over multiple periods. The proposed model pursues three objectives: minimizing service time, minimizing facility costs, and maximizing supply chain reliability. A scenario-based robust optimization approach is applied to model uncertainties in parameters such as demand and route statuses. The results show that the model is capable of fully allocating central warehouse inventory and preventing shortages under the highest demand scenario. The high allocation of fleet to the backup warehouse demonstrates the central role of this facility in network resilience. Sensitivity analysis also indicates that investment in warehouse fortification not only increases reliability but also significantly reduces drug purchasing and holding costs. Due to the NP-hard nature of the problem, the model is solved using GAMS for small-scale problems and the Non-dominated Sorting Genetic Algorithm (NSGA-II) for large-scale problems. Comparison of results shows that the metaheuristic algorithm provides acceptable quality Pareto solutions in dimensions where the exact method fails to solve. Conducting a real case study with actual data is suggested as an essential step for future research.

The results show that the model is capable of fully allocating central warehouse inventory and preventing shortages under the highest demand scenario. The high allocation of fleet to the backup warehouse demonstrates the central role of this facility in network resilience. Sensitivity analysis also indicates that investment in warehouse fortification not only increases reliability but also significantly reduces drug purchasing and holding costs. Due to the NP-hard nature of the problem, the model is solved using GAMS for small-scale problems and the Non-dominated Sorting Genetic Algorithm (NSGA-II) for large-scale problems. Comparison of results shows that the metaheuristic algorithm provides acceptable quality Pareto solutions in dimensions where the exact method fails to solve. Conducting a real case study with actual data is suggested as an essential step for future research.

Highlights

  • The pharmaceutical logistics network is designed in disaster preparedness and response phases.
  • Reliability is modeled by considering the failure probability of warehouses and routes.
  • The model is solved for large-scale problems using the NSGA-II algorithm.

Keywords
Pharmaceutical Logistics
Backup and Temporary Warehouse
Robust Optimization
Reliability
Disaster Management
Subjects

Copyright © Hamid Moakedi, Fatemeh Ghasemizadeh

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

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System Engineering and Productivity

Articles in Press, Accepted Manuscript
Available Online from 23 June 2026

  • Receive Date 28 April 2026
  • Revise Date 13 June 2026
  • Accept Date 23 June 2026
  • First Publish Date 23 June 2026
  • Publish Date 23 June 2026