research-article

Hyperheuristic encoding scheme for multi-objective guillotine cutting problems

Authors:

Jesica de Armas,

Coromoto LeónAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

Pages 1683 - 1690

https://doi.org/10.1145/2001576.2001803

Published: 12 July 2011 Publication History

Abstract

Most research on Strip Packing and Cutting Stock problems are focused on single-objective formulations of the problems. However, in this work we deal with more general and practical variants of the problems, which not only seeks to optimise the usage of the raw material, but also the overall production process.The problems target the cutting of a large rectangle in a set of smaller rectangles using orthogonal guillotine cuts. Common approaches are based in the minimisation of the strip length required to cut the whole set of demanded pieces (for strip problems) and in the maximisation of the total profit obtained from the available surface (for cutting stock problems). In this work we also deal with an extra objective which seeks to minimise the number of cuts involved in the cutting process, thus maximising the efficiency of the global production process. In order to obtain solutions to these problems, we have applied some of the most-known multi-objective evolutionary algorithms, since they have shown a promising behaviour when tackling multi-objective real-world problems. We have designed and implemented hyperheuristic-based encodings as an alternative to combine heuristics in such a way that a heuristic's strengths make up for the drawbacks of another.

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Cited By

Ramos-Maldonado MMuñoz FMora PVenegas-Vásconez D(2024)Optimizing Cutting Log Operations in Softwood Sawmills: A Multi-Objective Approach Tailored for SMEsIEEE Access10.1109/ACCESS.2024.345535112(128141-128150)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3455351
Neuenfeldt Júnior ASilva EFrancescatto MRosa CSiluk J(2022)The rectangular two-dimensional strip packing problem real-life practical constraintsComputers and Operations Research10.1016/j.cor.2021.105521137:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.cor.2021.105521
Mısır M(2021)Hyper-heuristics: Autonomous Problem SolversAutomated Design of Machine Learning and Search Algorithms10.1007/978-3-030-72069-8_7(109-131)Online publication date: 29-Jul-2021
https://doi.org/10.1007/978-3-030-72069-8_7
Show More Cited By

Index Terms

Hyperheuristic encoding scheme for multi-objective guillotine cutting problems
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Ramos-Maldonado MMuñoz FMora PVenegas-Vásconez D(2024)Optimizing Cutting Log Operations in Softwood Sawmills: A Multi-Objective Approach Tailored for SMEsIEEE Access10.1109/ACCESS.2024.345535112(128141-128150)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3455351
Neuenfeldt Júnior ASilva EFrancescatto MRosa CSiluk J(2022)The rectangular two-dimensional strip packing problem real-life practical constraintsComputers and Operations Research10.1016/j.cor.2021.105521137:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.cor.2021.105521
Mısır M(2021)Hyper-heuristics: Autonomous Problem SolversAutomated Design of Machine Learning and Search Algorithms10.1007/978-3-030-72069-8_7(109-131)Online publication date: 29-Jul-2021
https://doi.org/10.1007/978-3-030-72069-8_7
Virk ASingh K(2019)Application of Nature Inspired Algorithms to Optimize Multi-objective Two-Dimensional Rectangle Packing ProblemJournal of Industrial Integration and Management10.1142/S242486221950010604:04(1950010)Online publication date: 14-Oct-2019
https://doi.org/10.1142/S2424862219500106
Gomez JTerashima-Marín H(2018)Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problemsGenetic Programming and Evolvable Machines10.1007/s10710-017-9301-419:1-2(151-181)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s10710-017-9301-4
Maashi MÖzcan EKendall G(2014)A multi-objective hyper-heuristic based on choice functionExpert Systems with Applications: An International Journal10.1016/j.eswa.2013.12.05041:9(4475-4493)Online publication date: 1-Jul-2014
https://dl.acm.org/doi/10.1016/j.eswa.2013.12.050

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