A multi-objective approach for the 2D guillotine cutting stock problem

Authors:
Jesica de Armas;Gara Miranda;Coromoto León
Affiliations:
Universidad de La Laguna, Dpto. Estadística, I. O. y Computación, La Laguna, Spain;Universidad de La Laguna, Dpto. Estadística, I. O. y Computación, La Laguna, Spain;Universidad de La Laguna, Dpto. Estadística, I. O. y Computación, La Laguna, Spain
Venue:
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Year:
2011

Citing 6
Cited 0

Best-first search methods for constrained two-dimensional cutting stock problems

Operations Research
An improvement of Viswanathan and Bagchi's exact algorithm for constrained two-dimensional cutting stock

Computers and Operations Research
AllelesLociand the Traveling Salesman Problem

Proceedings of the 1st International Conference on Genetic Algorithms
A summary-attainment-surface plotting method for visualizing the performance of stochastic multiobjective optimizers

ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
A New Placement Heuristic for the Orthogonal Stock-Cutting Problem

Operations Research
A survey and comparison of guillotine heuristics for the 2D oriented offline strip packing problem

Discrete Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.