Welcome!
To use the personalized features of this site, please log in or register.
If you have forgotten your username or password, we can help.
My Menu
Saved Items

2-Opt Population Training for Minimization of Open Stack Problem

Alexandre César Muniz de OliveiraContact Information and Luiz Antonio Nogueira LorenaContact Information

(3)  DEINF/UFMA, Av. dos Portugueses, 65.085-580 São Luís MA, Brasil
(4)  LAC/INPE, Av. dos Astronautas, 12.201-970 São José dos Campos SP, Brasil
Abstract
This paper describes an application of a Constructive Genetic Algorithm (CGA) to the Minimization Open Stack Problem (MOSP). The MOSP happens in a production system scenario, and consists of determining a sequence of cut patterns that minimizes the maximum number of opened stacks during the cutting process. The CGA has a number of new features compared to a traditional genetic algorithm, as a population of dynamic size composed of schemata and structures that is trained with respect to some problem specific heuristic. The application of CGA to MOSP uses a 2-Opt like heuristic to define the fitness functions and the mutation operator. Computational tests are presented using available instances taken from the literature.

Contact Information Alexandre César Muniz de Oliveira
Email: acmo@deinf.ufma.br

Contact Information Luiz Antonio Nogueira Lorena
Email: lorena@lac.inpe.br
Fulltext Preview (Small, Large)
Image of the first page of the fulltext

References secured to subscribers.



Export this chapter
Export this chapter as RIS | Text
 
Remote Address: 38.107.191.106 • Server: mpweb20
HTTP User Agent: CCBot/1.0 (+http://www.commoncrawl.org/bot.html)