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A biased random‐key genetic algorithm for the minimization of open stacks problem
Author(s) -
Gonçalves José Fernando,
Resende Mauricio G. C.,
Costa Miguel Dias
Publication year - 2014
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12109
Subject(s) - key (lock) , minification , computer science , sequence (biology) , genetic algorithm , algorithm , mathematical optimization , function (biology) , quality (philosophy) , process (computing) , fitness function , mathematics , machine learning , computer security , epistemology , evolutionary biology , biology , genetics , operating system , philosophy
Abstract This paper describes a biased random‐key genetic algorithm ( BRKGA ) for the minimization of the open stacks problem ( MOSP ). The MOSP arises in a production system scenario, and consists of determining a sequence of cutting patterns that minimize the maximum number of open stacks during the cutting process. The proposed approach combines a BRKGA and a local search procedure for generating the sequence of cutting patterns. A novel fitness function for evaluating the quality of the solutions is also developed. Computational tests are presented using available instances taken from the literature. The high quality of the solutions obtained validate the proposed approach.