Multi-Criteria Optimization of Vaccine Supply Chain in Low-and Middle- Income Countries

With increasing population in developing countries and introduction of new vaccines (such as Rotavirus and Pneumococcal vaccines) that are more expensive, designing reliable and green vaccine supply chain is vital. This is especially important because the new vaccines are of greater volume and require significantly more packaging and cold storage in comparison to traditional vaccines. The existing cold storages capacity and transportation plans are not sufficient in many low- and middle- income countries for meeting their immunization demands. This study suggests economic and ecofriendly alternatives for vaccine supply chain through multi-criteria optimization using Non-Dominated Sorted Genetic Algorithm. The modeled supply chain includes manufacturing of vaccine, transportation, cold storage and disposal. We consider different technology options for each section with varying environmental emissions and costs. The proposed method evaluates the optimal combination of technology options. The model considers the demand for immunization as a function of time and this demand is based on infectious disease spread in developing countries. The results presented would be helpful for governments, policy makers and donors (e.g. UNICEF, WHO, GAVI Alliance) to design future immunization supply chains reducing both environmental burden and cost.