Multi-objective mathematical modelling approaches for common area layouts under social distancing

Although the impact of COVID-19 decreased and increased from time to time, it has changed our habits and lifestyle. Thanks to the concept of "social distance" having rejoined our lives, people can continue to settle more decently in closed and crowded areas and continue to be together despite the pandemic. To ensure this allocation under social distancing, mathematical methods were used in this thesis, in which it was discussed how many people can allocate in a place while trying to minimize the risk of total infection. 4 different AUGMECON methods, knowns as advanced versions of the epsilon-constraint method in the literature, have been applied to this kind of social distance-based optimization problem for the first time in the literature. In the problem where 3 different social distance metrics are used, 1 large and 4 small datasets taken from real life are used. While it was discovered that the AUG-R method is strong in terms of time and results for 216 points and more. On the other hand, the A-AUG2 method is very powerful in smaller datasets in terms of the richness of the solutions, thanks to solved models in GAMS optimization program.