ABSTRACT
It is important to define optimal supply chain strategies that can respond to real vaccination needs in different disasters, especially in the event of a pandemic. The distribution of medicines and vaccines is more critical when they can decay and must arrive at their final destination as fast as possible. In this paper, to overcome these problems and respond to the pandemic of COVID-19 needs, we introduced a bi-objective model for the distribution of COVID-19 vaccines. The objectives are to minimize cost function and to minimize the maximum traveling time of the vaccines to treat targeted populations in different time phases. The bi-objective model is solved with the well-known multi-objective augmented epsilon-constraint method. Besides, we bring numerical results and the appliance of our proposed model. By solving the proposed model, we can find the optimal network of the vaccines and open needed facilities in several locations. Finally, we give the decision-maker several possible answers to choose according to his preferences. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The need for a good supply chain distribution of vaccines in disasters is critical in today’s world. This paper targets the vaccines’ allocation in several periods for various and separated demand zones. To discover and make the al-location network more efficient and help the policymaker decide more wisely, we implement a smart contract to suggest product allocation in different cases taking into account dynamical changes. Our smart contract tries to decrease the uncovered demands and facilitate the governments’ prediction of the demands and evolution of a disease in demand zones. In this manner, several reactions of the smart contract are considered through different scenarios that can happen in the pandemics. The cases are sudden increase, sudden decrease, and steady demands. Moreover, we discuss the validity of the proposed smart contract and its benefits. Finally, we discuss the future aspects of our work and other objectives that can be useful in the proposed problem. © 2021, IFIP International Federation for Information Processing.