Algoritmo discreto del búfalo africano para resolver el problema de corte de material

Irving Barragan-Vite; Leonardo Javier Montiel-Arrieta; Juan Carlos Seck-Tuoh-Mora; Norberto Hernández-Romero; Joselito Medina-Marin

doi:10.29057/icbi.v11iEspecial3.11489

Autores/as

Irving Barragan-Vite Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-3625-6943
Leonardo Javier Montiel-Arrieta Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0002-9259-535X
Juan Carlos Seck-Tuoh-Mora Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-3678-1120
Norberto Hernández-Romero Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0001-9942-306X
Joselito Medina-Marin Universidad Autónoma del Estado de Hidalgo https://orcid.org/0000-0003-0937-8707

DOI:

https://doi.org/10.29057/icbi.v11iEspecial3.11489

Palabras clave:

problema de corte, metaheurística, discretización, problema combinatorio

Resumen

El problema de corte abordado en este documento consiste en minimizar el desperdicio total producido al cortar un conjunto de piezas pequeñas en una secuencia determinada a partir de piezas de material más grandes. El algoritmo del búfalo Africano ha sido empleado exitosamente para resolver problemas de tipo combinatorio. Una de las dificultades de este algoritmo es generar soluciones discretas para esta clase de problemas discretos. En este trabajo se emplea una variante discreta del algoritmo del búfalo Africano en la que se compara una técnica de cruza así como la técnica del valor del orden clasificado para obtener soluciones discretas. Se usa un conjunto de diez instancias de diferente complejidad para realizar la comparación de estas técnicas. Los resultados muestran que la técnica de cruza supera a las otras en cuanto a la calidad de las soluciones. Luego estos resultados se comparan contra otros algoritmos para evaluar su desempeño.

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Editora:: Universidad Autónoma del Estado de Hidalgo

Citas

Alfares, H. K., & Alsawafy, O. G. (2019). A least-loss algorithm for a biobjective one-dimensional cutting-stock problem. International Journal of Applied Industrial Engineering (IJAIE), 6(2), 1–19.

Araujo, S. A. d., Poldi, K. C., & Smith, J. (2014). A genetic algorithm for the one-dimensional cutting stock problem with setups. Pesquisa Operacional, 34(2), 165–187.

Asvany, T., Amudhavel, J., & Sujatha, P. (2017). One-dimensional cutting stock problem with single and multiple stock lengths using DPSO. Advances and Applications in Mathematical Sciences, 17(4), 147–163.

Ben Lagha, G., Dahmani, N., & Krichen, S. (2014). Particle swarm optimization approach for resolving the cutting stock problem. In 2014 International Conference on Advanced Logistics and Transport (ICALT) (pp. 259–263).

Berberler, M., & Nuriyev, U. (2010). A new heuristic algorithm for the one-dimensional cutting stock problem. Applied and Computational Mathematics, 9(1), 19–30.

Chen, W.-N., & Tan, D.-Z. (2018). Set-based discrete particle swarm optimization and its applications: a survey. Frontiers of Computer Science: Selected Publications from Chinese Universities, 12(2).

Chen, Y.-H., Huang, H.-C., Cai, H.-Y., & Chen, P.-F. (2019). A genetic algorithm approach for the multiple length cutting stock problem. In 2019 IEEE 1st Global Conference on Life Sciences and Technologies (LifeTech) (pp. 162–165).

Chiong, R., & Beng, O. K. (2007). A comparison between genetic algorithms and evolutionary programming based on cutting stock problem. Engineering Letters, 14(1), 72–77.

Delorme, M., Iori, M., & Martello, S. (2016). Bin packing and cutting stock problems: Mathematical models and exact algorithms. European Journal of Operational Research, 255(1), 1–20.

Evtimov, G., & Fidanova, S. (2018). Ant colony optimization algorithm for 1D cutting stock problem. In Georgiev, K., Todorov, M., & Georgiev, I. (Eds.), Advanced Computing in Industrial Mathematics: 11th Annual Meeting of the Bulgarian Section of SIAM, December 20-22, 2016, Sofia, Bulgaria. Revised Selected Papers (pp. 25–31). Springer International Publishing.

Falkenauer, E. (1996). A hybrid grouping genetic algorithm for bin packing. Journal of Heuristics, 2, 5–30.

Fang, J., Rao, Y., Luo, Q., & Xu, J. (2023). Solving one-dimensional cutting stock problems with deep reinforcement learning. Mathematics, 11(4), 1028.

Foerster, H., & Wascher, G. (2000). Pattern reduction in one-dimensional cutting stock problems. International Journal of Production Research, 38(7), 1657–1676.

Gherboudj, A. (2019). African buffalo optimization for one-dimensional bin packing problem. International Journal of Swarm Intelligence Research (IJSIR), 10(4), 38–52.

Gilmore, P. C., & Gomory, R. E. (1961). A linear programming approach to the cutting-stock problem. Operations Research, 9(6), 849–859.

Gilmore, P. C., & Gomory, R. E. (1963). A linear programming approach to the cutting stock problem—Part II. Operations Research, 11(6), 863–888.

Gradišar, M., Kljajić, M., Resinovič, G., & Jesenko, J. (1999). A sequential heuristic procedure for one-dimensional cutting. European Journal of Operational Research, 114(3), 557–568.

Haessler, R. W. (1992). One-dimensional cutting stock problem problems and solution procedures. Mathematical and Computer Modelling, 16(1), 1–8.

Hinterding, R., & Khan, L. (1995). Genetic algorithms for cutting stock problems: With and without contiguity. In Yao, X. (Ed.), Progress in Evolutionary Computation (pp. 166–186). Springer Berlin Heidelberg.

Jahromi, M. H., Tavakkoli-Moghaddam, R., Makui, A., & Shamsi, A. (2012). Solving a one-dimensional cutting stock problem by simulated annealing and tabu search. Journal of Industrial Engineering International, 8(24).

Jiang, T., Zhang, C., & Sun, Q.-M. (2019). Green job shop scheduling problem with discrete whale optimization algorithm. IEEE Access, 7, 43153–43166.

Jiang, T., Zhu, H., & Deng, G. (2020). Improved African buffalo optimization algorithm for the green flexible job shop scheduling problem considering energy consumption. Journal of Intelligent & Fuzzy Systems, 38(4), 4573–4579.

Kantorovich, L. V. (1960). Mathematical methods of organizing and planning production. Management Science, 6(4), 366–422.

Krisnawati, M., Sibarani, A. A., Mustikasari, A., & Aulia, D. (2020). African buffalo optimization for solving flow shop scheduling problem to minimize makespan. IOP Conference Series: Materials Science and Engineering, (1), 012061.

Lee, D., Son, S., Kim, D., & Kim, S. (2020). Special-length-priority algorithm to minimize reinforcing bar-cutting waste for sustainable construction. Sustainability, 12(15).

Levine, J., & Ducatelle, F. (2004). Ant colony optimization and local search for bin packing and cutting stock problems. Journal of the Operational Research Society, 55(7), 705–716.

Li, Y., Zheng, B., & Dai, Z. (2007). General particle swarm optimization based on simulated annealing for multi-specification one-dimensional cutting stock problem. In Wang, Y., Cheung, Y.-m., & Liu, H. (Eds.), Computational Intelligence and Security (pp. 67–76). Springer Berlin Heidelberg.