A Multi-objective Approach for the 2D Guillotine Cutting Stock Problem

This work presents a multi-objective approach to solve the Constrained 2D Cutting Stock Problem. The problem targets the cutting of a large rectangle of fixed dimensions in a set of smaller rectangles using orthogonal guillotine cuts. Although the problem is usually focused on a single objective, in this work we want to optimise the layout of rectangular parts on the sheet of raw material so as to maximise the total profit, as well as minimise the number of cuts to achieve the final demanded pieces. For this, we apply Multi-Objective Evolutionary Algorithms given its great effectiveness when dealing with other types real-world multi-objective problems. For the problem solution, we have implemented an encoding scheme which uses a post-fix notation. According to the two different optimisation criteria the approach provides a set of solutions offering a range of trade-offs between the two objectives, from which clients can choose according to their needs.