Coordinated COVID-19 vaccination scheduling model by using nearest distance-single course timetabling method

Abstract

This work proposes a new coordinated vaccine scheduling model suitable for the city size COVID-19 vaccination program. It is different from the existing COVID-19 vaccination scheduling mechanism where there is no coordination among endpoint providers. On the other side, the vaccine stock in every provider is limited, so that this mismatch creates many unserved participants. Moreover, studies on the COVID-19 vaccination scheduling problem are hard to find. This work aims to solve this mismatch problem. It is developed by combining the nearest distance and the single course timetabling. It is then optimized by using a cloud theory based-simulated annealing algorithm. The simulation result shows that the proposed model outperforms both the uncoordinated and basic course timetabling models. It can minimize the number of unserved participants, total travel distance, and the number of participants with missed timeslot. It produces zero unserved participants if the total vaccine quantity is at least equal to the total number of participants. The proposed model creates lower total travel distance than the uncoordinated or basic course timetabling adopted model. It is also better than the basic course timetabling model in creating a low number of participants with missed timeslot.