A centralized solution to the student-school assignment problem in segregated environments via a CUDA parallelized simulated annealing algorithm

In this work, we implemented a CUDA parallelized simulated annealing algorithm to solve the student-school assignment problem in a highly segregated environment. The objective function optimized considered the average distance from the students to their assigned school, the socio-economic segregation via the dissimilarity index, and the cost of schools partially filled. Using data from the MINEDUC, the INE, and the Municipality of Temuco (Chile), we simulated the distribution of Temuco’s student population, solving its students’ assignment to the city’s schools (29853 students to 85 schools). The results obtained were better with a high number of block (simultaneous students exploring), and a low number of threads (simultaneous schools explored by these students) instantiated in the GPU. Algorithm execution time worsens with the number of blocks and the number of threads, although it remained below 1000 seconds in the worst and below 400 seconds in the best case. However, the algorithm achieves excellent results in reducing socio-economic segregation, taking it from a high level to almost making it disappear. We achieved this result, even with a reduction of the average distance from students to their assigned school.