The nesting problem of 2D shapes, which has impactful application in the cutting and packing fields, has been studied for many years. Previous papers are mainly focused on proposing new algorithms and prove their efficiency in terms of packing density or computation time. However, the results are reported only on few datasets and the comparison is done only with respect to few competing algorithms. The aim of the paper is to analyse and compare the results obtained by strip-packing algorithms published in the last 20 years. The results show that the effectiveness of the algorithms varies widely across different datasets, and there is a lack of comprehensive benchmarking that considers both the quality of solution and the computational time required to achieve it. Furthermore, since no algorithm clearly outperforms all the others, further methods to address the nesting problem with reinforcement learning and neural networks could be investigated to improve the generalization ability on the nesting problem.