Welcome to the InfoSci Platform
A Controlled Stability Genetic Algorithm With the New BLF2G Guillotine Placement Heuristic for the Orthogonal Cutting-Stock Problem

A Controlled Stability Genetic Algorithm With the New BLF2G Guillotine Placement Heuristic for the Orthogonal Cutting-Stock Problem

Slimane Abou-Msabah, Ahmed-Riadh Baba-Ali, Basma Sager
Copyright: © 2019 |Volume: 13 |Issue: 4 |Pages: 21
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781522564591|DOI: 10.4018/IJCINI.2019100105
Cite Article Cite Article

MLA

Abou-Msabah, Slimane, et al. "A Controlled Stability Genetic Algorithm With the New BLF2G Guillotine Placement Heuristic for the Orthogonal Cutting-Stock Problem." IJCINI vol.13, no.4 2019: pp.91-111. http://doi.org/10.4018/IJCINI.2019100105

APA

Abou-Msabah, S., Baba-Ali, A., & Sager, B. (2019). A Controlled Stability Genetic Algorithm With the New BLF2G Guillotine Placement Heuristic for the Orthogonal Cutting-Stock Problem. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 13(4), 91-111. http://doi.org/10.4018/IJCINI.2019100105

Chicago

Abou-Msabah, Slimane, Ahmed-Riadh Baba-Ali, and Basma Sager. "A Controlled Stability Genetic Algorithm With the New BLF2G Guillotine Placement Heuristic for the Orthogonal Cutting-Stock Problem," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 13, no.4: 91-111. http://doi.org/10.4018/IJCINI.2019100105

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

The orthogonal cutting-stock problem tries to place a given set of items in a minimum number of identically sized bins. Combining the new BLF2G heuristic with an advanced genetic algorithm can help solve this problem with the guillotine constraint. According to the item order, the BLF2G heuristic creates a direct placement of items in bins to give a cutting format. The genetic algorithm exploits the search space to find the supposed optimal item order. Other methods try to guide the evolutionary process. A new enhancement guides the evolutionary process, enriching the population via qualified individuals, without disturbing the genetic phase. The evolution of the GA process is controlled, and when no improvements after some number of iterations are observed, a qualified individual is injected to the population to avoid premature convergence to a local optimum. A generated set of order-based individuals enriches the evolutionary process with qualified chromosomes. The proposed method is compared with other heuristics and metaheuristics found in the literature on existing data sets.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.