An improved metaheuristic algorithm for maximizing demand satisfaction in the population harvest cutting stock problem

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Abstract

We present a greedy version of an existing metaheuristic algorithm for a special version of the Cutting Stock Problem (CSP). For this version, it is only possible to have indirect control over the patterns via a vector of continuous values which we refer to as a weights vector. Our algorithm iteratively generates new weights vectors by making local changes over the best weights vector computed so far. This allows us to achieve better solutions much faster than is possible with the original metaheuristic.

Original languageEnglish
Title of host publicationProceedings of the 9th Annual Symposium on Combinatorial Search, SoCS 2016
EditorsJorge A. Baier, Adi Botea
PublisherAssociation for the Advancement of Artificial Intelligence
Pages127-128
Number of pages2
ISBN (Electronic)9781577357698
DOIs
Publication statusPublished - 2016
Event9th Annual Symposium on Combinatorial Search, SoCS 2016 - Tarrytown, United States
Duration: 6 Jul 20168 Jul 2016

Publication series

NameProceedings of the 9th Annual Symposium on Combinatorial Search, SoCS 2016
Volume2016-January

Conference

Conference9th Annual Symposium on Combinatorial Search, SoCS 2016
Country/TerritoryUnited States
CityTarrytown
Period6/07/168/07/16

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