Hostname: page-component-7dd5485656-zlgnt Total loading time: 0 Render date: 2025-10-31T16:38:36.293Z Has data issue: false hasContentIssue false
  • English
  • Français

Heuristic to optimize L-guillotine cutting operations

Published online by Cambridge University Press:  22 July 2005

ALBERTO GÓMEZ ,
DAVID DE LA FUENTE ,
PAOLO PRIORE  and
JAVIER PUENTE
ALBERTO GÓMEZ
Affiliation:
Departamento de Admon de Empresas y Contabilidad, Escuela Politécnica Superior de Ingeniería de Gijón, Universidad de Oviedo, Edificio de Energía, 33204 Gijón, Asturias, Spain
DAVID DE LA FUENTE
Affiliation:
Departamento de Admon de Empresas y Contabilidad, Escuela Politécnica Superior de Ingeniería de Gijón, Universidad de Oviedo, Edificio de Energía, 33204 Gijón, Asturias, Spain
PAOLO PRIORE
Affiliation:
Departamento de Admon de Empresas y Contabilidad, Escuela Politécnica Superior de Ingeniería de Gijón, Universidad de Oviedo, Edificio de Energía, 33204 Gijón, Asturias, Spain
JAVIER PUENTE
Affiliation:
Departamento de Admon de Empresas y Contabilidad, Escuela Politécnica Superior de Ingeniería de Gijón, Universidad de Oviedo, Edificio de Energía, 33204 Gijón, Asturias, Spain
Get access Rights & Permissions [Opens in a new window]

Abstract

This study presents an application to optimize the use of an L-cut guillotine machine. The application has two distinct parts to it; first, a number of rectangular shapes are placed on as few metal sheets as possible by using genetic algorithms. Second, the sequence for cutting these pieces has to be generated. The guillotine's numeric control then uses this sequence to make the cuts.

Keywords

Genetic AlgorithmsHeuristicOptimizationPacking

Information

Type
PRACTICUM PAPER
Information
AI EDAM , Volume 19 , Issue 1 , February 2005 , pp. 33 - 38
Copyright
2005 Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

Baker, J.E. (1987). Reducing bias and inefficiency in the selection algorithm. Proc. Second Int. Conf. Genetic Algorithms, pp. 14–21.
Davis, L. (1991). Handbook of Genetic Algorithms. New York: Van Nostrand Reinhold.
Dyckhoff, H. (1990). A typology of cutting and packing problems. European Journal of Operational Research 44, 145–159.CrossRefGoogle Scholar
Gilmore, P.C. & Gomory, R.E. (1961). A linear programming approach to the cutting-stock problem. Operations Research 9, 724–746.CrossRefGoogle Scholar
Gilmore, P.C. & Gomory, R.E. (1963). A linear programming approach to the cutting stock problem. Operations Research 11, 863–888.CrossRefGoogle Scholar
Gilmore, P.C. & Gomory, R.E. (1965). Multi stage cutting stock problems of two and more dimensions. Operations Research 13, 94–1120.CrossRefGoogle Scholar
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization & Machine Learning. Reading, MA: Addison–Wesley.
Goldberg, D.E. & Lingle, R. (1985). Alleles, loci, and the TSP. Proc. First Int. Conf. Genetic Algorithms, pp. 154–159. Hillsdale, NJ: Erlbaum.
Gómez, A. & De la Fuente, D. (2000). Resolution of strip-packing problems with genetic algorithms. Journal of the Operational Research Society 51, 1289–1295.CrossRefGoogle Scholar
Gómez, A., De la Fuente, D., & Priore, P. (2000). Resolución del problema de strip-packing mediante la metaheurística algoritmos genéticos. Boletín de la SEIO 16(2), 12–16.Google Scholar
Healy, P., Creavin, M., & Kuusik, A. (1999). An optimal algorithm for rectangle placement. Operations Research Letters 24, 73–80.CrossRefGoogle Scholar
Holland, H.J. (1975). Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
Hopper, E. & Turton, B.C.H. (2001). A review of the application of meta-heuristic algorithms to 2D strip packing problems. Artificial Intelligence Review 16(4), 257–300.CrossRefGoogle Scholar
Jakobs, S. (1996). On genetic algorithms for the packing of polygons. European Journal of Operational Research 88, 165–181.CrossRefGoogle Scholar
Lai, K.K. & Chan, J.W.M. (1997). Developing a simulated annealing algorithm for the cutting stock problem. Computers & Industrial Engineering 32, 115–127.CrossRefGoogle Scholar
Leung, T.W., Yung, C.H., & Troutt, M.D. (2001). Applications of genetic search and simulated annealing to the two-dimensional non-guillotine cutting stock problem. Computers & Industrial Engineering 40, 201–214.CrossRefGoogle Scholar
Lodi, A., Martello, S., & Monaci, M. (2002). Two-dimensional packing problems: a survey. European Journal of Operational Research 141(2), 241–252.CrossRefGoogle Scholar
Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs. New York: Springer–Verlag.CrossRef