Optimization models for vaccine distribution and allocation networks
Puneet Kumar Yadavpuneet.yadav1@niu.edu.inDepartment of Computer Science & EngineeringNoida International UniversityGreater Noida, Uttar Pradesh, 201312, IndiaView full profile → , *Shrushti DeshmukhCorresponding authorsrushti.deshmukh@vit.eduDepartment of Electronics and Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , R. Srikanthroyyurusrikanth@vardhaman.orgDepartment of Computer Science and EngineeringVardhaman College of EngineeringShamshabad, Hyderabad, Telangana, 501218, IndiaView full profile → , K. Parimalaparim@maher.ac.inDepartment of PharmacologyMeenakshi Medical College Hospital and Research InstituteMeenakshi Academy of Higher Education & ResearchChennai, Tamil Nadu, 600087, IndiaView full profile → , V. Anantha Lakshmiananthalaxmi@outlook.comDepartment of Computer Science and Engineering (Artificial Intelligence & Machine Learning)Pragati Engineering CollegeKakinada, Andhra Pradesh, 533437, IndiaView full profile → , Yogesh Nagargojesymbiosisyogesh@gmail.comResearcher Connect Innovation and Impact Private LimitedNagpur, Maharashtra, 440028, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2026
- Published Online:
- 31 Oct 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2491
- Pages:
- 3645–3656
Abstract
The distribution of vaccines is constrained by a number of practical issues such as uncertainty about vaccine demand, small stock holdings, inadequate cold-chain capacity, logistical delays, and access to vaccines across regions. In this study, an implementable optimization model is developed to optimize the allocation of vaccines in the regional warehouses and health centers. A Multi-Objective Mixed-Integer Linear Programming (MO-MILP) model is used to reduce the transportation cost, delivery delay and enhance the demand fulfilment. Available vaccine stock, vaccine vehicle capacity, stock and storage capacity, regional demand, priority groups, travel distance and delivery schedules are incorporated into the methodology. Simulated distributions for various regions reveal a decrease of 11.8% in transportation cost, 14.6% in mean delivery time and an improvement in demand fulfilment from 89.7% to 95.2% as proven through experimental analysis. The innovation is that the logistics, equity and cold chain considerations are all integrated in one allocation model. The proposed framework offers a scalable decision support tool for realistic healthcare planning and decision making on vaccines, and suggests that optimized allocation would enable a more efficient distribution and better access to vaccines.
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References
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