This article discusses cutting and packing problems found in the shipbuilding industry, with the aim of minimizing material wastage. The irregular nesting problem is a variation of such problems in which the container has variable length and fixed width. Nesting of irregular shapes is a combinatorial problem with nondeterministic polynomial complete difficulty. Generally, work on solving such problems is mainly conducted in the fields of geometry and operations research. This study proposes a new method for solving the nesting problem; i.e., shape nesting can be simulated by the motion of discrete bodies in a container with a fixed width. Calculation with the program developed by the authors has been performed, and the results show that the algorithm was able to achieve good compaction for the datasets, and the running time and utility of sheet are satisfied. Results of this study show that the new method based on motion simulation can be a new solution to the shape nesting problem.
Algorithm Based on Deep Reinforcement Learning for Irregular Shape Nesting Problem
