COVID-19 vaccine distribution time optimization with Genetic Algorithm

Covid-19 emerged in 2019 in Wuhan, China, and spread from there to the whole world. Some vaccines have been
developed by scientists to combat this virus. These vaccines were distributed to hospitals and health centres to be
delivered to people. Vaccines are distributed in special conditions by special vehicles without spoiling. Distribution
of vaccines can be thought of as a traveling salesman problem. The traveling salesman problem is an NP-hard type
problem. It is not possible to solve it mathematically in normal time. For this reason, genetic algorithm, which is
a very popular algorithm, was used to create the vaccine distribution route in this study. The genetic algorithm is
an algorithm that works on the principle of the fittest survives. In the genetic algorithm, a random population
containing a certain number of solutions is first created. This population undergoes metamorphosis through the
stages of selection, crossover, and mutation. The good ones from the new solutions created are transferred to the
next generation and thus, better solutions are obtained. In the traveling salesman problem, the solution sequence
is tested with the solutions generated by the genetic algorithm, and the solution that distributes in the shortest time
is recorded as the best solution. Thus, the distribution of the vaccine is carried out as soon as possible. With the
genetic algorithm, it is aimed to determine the shortest time for vaccine distribution to health centres in Kayseri.
The solution obtained is quite successful.
Key Words: Covid-19, Genetic Algorithm, Traveling Salesman Problem, Optimization