Optimization in Sanger sequencing

•The main objective of the paper is to solve the optimization problem associated with the classification of DNA samples in PCR plates for Sanger sequencing.•We designed an integer linear program that can only be solved for small instances.•We proposed a simulated annealing algorithm which provides reasonable solutions in short time.•We considered real datasets and compare our heuristic algorithm with the solutions previously obtained in the laboratory.•The algorithm is being succesfully used in the laboratories of the company Health in Code. The main objective of this paper is to solve the optimization problem that is associated with the classification of DNA samples in PCR plates for Sanger sequencing. To achieve this goal, we design an integer linear programming model. Given that the real instances involve the classification of thousands of samples and the linear model can only be solved for small instances, the paper includes a heuristic to cope with bigger problems. The heuristic algorithm is based on the simulated annealing technique. This algorithm obtains satisfactory solutions to the problem in a short amount of time. It has been tested with real data and yields improved results compared to some commercial software typically used in (clinical) laboratories. Moreover, the algorithm has already been implemented in the laboratory and is being successfully used.