- -

Análisis multi-objetivo del problema de asignación del buffer con meta-modelos de simulación y una metaheurística híbrida

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

  • Estadisticas de Uso

Análisis multi-objetivo del problema de asignación del buffer con meta-modelos de simulación y una metaheurística híbrida

Show full item record

Hernández-Vázquez, JO.; Hernández-González, S.; Hernández-Vázquez, JI.; Jiménez-García, JA.; Hernández-Ripalda, MD. (2022). Análisis multi-objetivo del problema de asignación del buffer con meta-modelos de simulación y una metaheurística híbrida. Revista Iberoamericana de Automática e Informática industrial. 19(2):221-232. https://doi.org/10.4995/riai.2021.15731

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/182827

Files in this item

Item Metadata

Title: Análisis multi-objetivo del problema de asignación del buffer con meta-modelos de simulación y una metaheurística híbrida
Secondary Title: Multi-objective analysis of the buffer allocation problem with simulation meta-models and a hybrid metaheuristic
Author: Hernández-Vázquez, José Omar Hernández-González, Salvador Hernández-Vázquez, José Israel Jiménez-García, José Alfredo Hernández-Ripalda, Manuel Darío
Issued date:
Abstract:
[EN] This article presents a multi-objective formulation of the buffer allocation problem (BAP) in a serial-parallel production line, which aims to maximize the throughput rate and minimize the total cost of the allocation ...[+]


[ES] Este artículo presenta una formulación multi-objetivo del problema de asignación del buffer (BAP, por sus siglas en inglés) en una línea de producción paralela en serie, que pretende maximizar la tasa promedio de ...[+]
Subjects: Buffer allocation problem (BAP) , Meta-models , Hybrid metaheuristic , Optimization , Production line , Problema de asignación del buffer , Meta-modelos , Metaheurística híbrida , Optimización , Línea de producción
Copyrigths: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Source:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2021.15731
Publisher:
Universitat Politècnica de València
Publisher version: https://doi.org/10.4995/riai.2021.15731
Project ID:
info:eu-repo/grantAgreement/CONACyT//375571/
Thanks:
Se agradece al Consejo Nacional de Ciencia y Tecnología (CONACYT) por el financiamiento de esta investigación con número de registro CVU: 375571; y al Tecnológico Nacional de México / Instituto Tecnológico de Celaya, por ...[+]
Type: Artículo

References

Abdul-Kader, W., Ganjavi, O., & Baki, F. (2011). A nonlinear model for optimizing the performance of a multi-product production line. International Transactions in Operational Research, 18(5), 561-577. https://doi.org/10.1111/j.1475-3995.2011.00814.x

Alaouchiche, Y., Ouazene, Y. & Yalaoui, F. (2021). Energy-efficient buffer allocation problem in unreliable production lines. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-021-06971-1

Alfieri, A., Matta, A., & Pastore, E. (2020). The time buffer approximated Buffer Allocation Problem: A row-column generation approach. Computers and Operations Research, 115, 104835. https://doi.org/10.1016/j.cor.2019.104835 [+]
Abdul-Kader, W., Ganjavi, O., & Baki, F. (2011). A nonlinear model for optimizing the performance of a multi-product production line. International Transactions in Operational Research, 18(5), 561-577. https://doi.org/10.1111/j.1475-3995.2011.00814.x

Alaouchiche, Y., Ouazene, Y. & Yalaoui, F. (2021). Energy-efficient buffer allocation problem in unreliable production lines. The International Journal of Advanced Manufacturing Technology. https://doi.org/10.1007/s00170-021-06971-1

Alfieri, A., Matta, A., & Pastore, E. (2020). The time buffer approximated Buffer Allocation Problem: A row-column generation approach. Computers and Operations Research, 115, 104835. https://doi.org/10.1016/j.cor.2019.104835

Amiri, M., & Mohtashami, A. (2012). Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm. International Journal of Advanced Manufacturing Technology, 62(1-4), 371-383. https://doi.org/10.1007/s00170-011-3802-8

Ariyani, A. K., Mahmudy, W. F., & Anggodo, Y. P. (2018). Hybrid genetic algorithms and simulated annealing for multi-trip vehicle routing problem with time windows. International Journal of Electrical and Computer Engineering, 8(6), 4713-4723. https://doi.org/10.11591/ijece.v8i6.pp4713-4723

Bamporiki, T., Bekker, J., & Yoon, M. (2019). Using a discrete-event, simulation optimisation optimiser to solve a stochastic multi-objective NP-hard problem. International Conference on Competitive Manufacturing, (February).

Bekker, J. (2013). Multi-objective buffer space allocation with the cross-entropy method. International Journal of Simulation Modelling, 12(1), 50-61. https://doi.org/10.2507/IJSIMM12(1)5.228

Blum, C., Blesa Aguilera, M. J., Roli, A., & Sampels, M. (2008). Hybrid metaheuristics an emerging approach to optimization. Berlin: Springer. https://doi.org/10.1007/978-3-540-78295-7

Chehade, H., Yalaoui, F., Amodeo, L., & Dugardin, F. (2010). Buffers sizing in assembly lines using a lorenz multiobjective ant colony optimization algorithm. 2010 International Conference on Machine and Web Intelligence, ICMWI 2010 - Proceedings, (2), 283-287. https://doi.org/10.1109/ICMWI.2010.5647916

Cruz, F. R. B., Kendall, G., While, L., Duarte, A. R., & Brito, N. L. C. (2012). Throughput maximization of queueing networks with simultaneous minimization of service rates and buffers. Mathematical Problems in Engineering, 2012. https://doi.org/10.1155/2012/692593

Cruz, F. R. B., Van Woensel, T., & Smith, J. M. G. (2010). Buffer and throughput trade-offs in M/G/1/K queueing networks: A bi-criteria approach. International Journal of Production Economics, 125(2), 224-234. https://doi.org/10.1016/j.ijpe.2010.02.017

Demir, L., Tunali, S., & Eliiyi, D. T. (2014). The state of the art on buffer allocation problem: A comprehensive survey. Journal of Intelligent Manufacturing, 25(3), 371-392. https://doi.org/10.1007/s10845-012-0687-9

Dengiz, B., & Akbay, K. S. (2000). Computer simulation of a PCB production line: Metamodeling approach. International Journal of Production Economics, 63(2), 195-205. https://doi.org/10.1016/S0925-5273(99)00013-4

Dolgui, A. B., Eremeev, A. V., & Sigaev, V. S. (2017). Analysis of a multicriterial buffer capacity optimization problem for a production line. Automation and Remote Control, 78(7), 1276-1289. https://doi.org/10.1134/S0005117917070098

Durieux, S., & Pierreval, H. (2004). Regression metamodeling for the design of automated manufacturing system composed of parallel machines sharing a material handling resource. International Journal of Production Economics, 89(1), 21-30. https://doi.org/10.1016/S0925-5273(03)00199-3

García Dunna, E., García Reyes, H., & Cárdenas Barrón, L. E. (2013). Simulación y análisis de sistemas con ProModel (Segunda ed.). México: Pearson.

Hernández-Vázquez, J. O., Hernández-González, S., Jiménez-García, J. A., Hernández-Ripalda, M. D., & Hernández-Vázquez, J. I. (2019). Enfoque híbrido metaheurístico AG-RS para el problema de asignación del buffer que minimiza el inventario en proceso en líneas de producción abiertas en serie. Revista Iberoamericana de Automática e Informática Industrial, 16(4), 447-458. https://doi.org/10.4995/riai.2019.10883

Hernandez-Vicen, J., Martinez, S., & Balaguer, C. (2021). Principios básicos para el desarrollo de una aplicación de bi-manipulación de cajas por un robot humanoide. Revista Iberoamericana de Automática e Informática industrial, 18(2), 129-137. https://doi.org/10.4995/riai.2020.13097

Kleijnen, J. P. C., & Sargent, R. G. (2000). A methodology for fitting and validating metamodels in simulation. European Journal of Operational Research, 120(1), 14-29. https://doi.org/10.1016/S0377-2217(98)00392-0

Köse, S. Y., Demir, L., Tunal, S., & Eliiyi, D. T. (2015). Capacity improvement using simulation optimization approaches: A case study in the thermotechnology industry. Engineering Optimization, 47(2), 149-164. https://doi.org/10.1080/0305215X.2013.875166

Kose, S. Y., & Kilincci, O. (2015). Hybrid approach for buffer allocation in open serial production lines. Computers & Operations Research, 60, 67-78. https://doi.org/10.1016/j.cor.2015.01.009

Kose, S. Y., & Kilincci, O. (2018). A multi-objective hybrid evolutionary approach for buffer allocation in open serial production lines. Journal of Intelligent Manufacturing, 1-19. https://doi.org/10.1007/s10845-018-1435-6

Koyuncuoğlu, M. U., & Demir, L. (2021). Buffer capacity allocation in unreliable production lines: An adaptive large neighborhood search approach. Engineering Science and Technology, an International Journal, 24(2), 299-309. https://doi.org/10.1016/j.jestch.2020.08.012

Li, J. (2013). Continuous improvement at Toyota manufacturing plant: Applications of production systems engineering methods. International Journal of Production Research, 51(23-24), 7235-7249. https://doi.org/10.1080/00207543.2012.753166

Lin, J. T., & Chiu, C. C. (2018). A hybrid particle swarm optimization with local search for stochastic resource allocation problem. Journal of Intelligent Manufacturing, 29(3), 481-495. https://doi.org/10.1007/s10845-015-1124-7

Mohtashami, A. (2014). A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters. International Journal of Advanced Manufacturing Technology, 74(9-12), 1577-1593. https://doi.org/10.1007/s00170-014-6098-7

Motlagh, M. M., Azimi, P., Amiri, M., & Madraki, G. (2019). An efficient simulation optimization methodology to solve a multi-objective problem in unreliable unbalanced production lines. Expert Systems with Applications, 138, 112836. https://doi.org/10.1016/j.eswa.2019.112836

Nahas, N. (2017). Buffer allocation and preventive maintenance optimization in unreliable production lines. Journal of Intelligent Manufacturing, 28(1), 85-93. https://doi.org/10.1007/s10845-014-0963-y

Nahas, N., & Nourelfath, M. (2018). Joint optimization of maintenance, buffers and machines in manufacturing lines. Engineering Optimization, 50(1), 37-54. https://doi.org/10.1080/0305215X.2017.1299716

Nahas, N., Nourelfath, M., & Gendreau, M. (2014). Selecting machines and buffers in unreliable assembly/disassembly manufacturing networks. International Journal of Production Economics, 154, 113-126. https://doi.org/10.1016/j.ijpe.2014.04.011

Narasimhamu, K. L., Venugopal Reddy, V., & Rao, C. S. P. (2014). Optimal buffer allocation in tandem closed queuing network with single server using PSO. Procedia Materials Science, 5, 2084-2089. https://doi.org/10.1016/j.mspro.2014.07.543

Noguera, J. H., & Watson, E. F. (2006). Response surface analysis of a multi-product batch processing facility using a simulation metamodel. International Journal of Production Economics, 102(2), 333-343. https://doi.org/10.1016/j.ijpe.2005.02.014

Oesterle, J., Bauernhansl, T., & Amodeo, L. (2016). Hybrid multi-objective optimization method for solving simultaneously the line balancing, equipment and buffer sizing problems for hybrid assembly systems. Procedia CIRP, 57, 416-421. https://doi.org/10.1016/j.procir.2016.11.072

Ouzineb, M., Mhada, F. Z., Pellerin, R., & El Hallaoui, I. (2018). Optimal planning of buffer sizes and inspection station positions. Production and Manufacturing Research, 6(1), 90-112. https://doi.org/10.1080/21693277.2017.1422812

Pantano, M., Fernández, M., Rodríguez, L., & Scaglia, G. (2021). Optimización dinámica basada en Fourier. Aplicación al proceso de biodiesel. Revista Iberoamericana de Automática e Informática industrial, 18(1), 32-38. https://doi.org/10.4995/riai.2020.12920

Patchong, A., & Kerbache, L. (2017). Transiting toward the factory of the future: Optimal buffer sizes and robot cell design in car body production. IEEE International Conference on Industrial Engineering and Engineering Management, 2017-Decem, 1596-1601. https://doi.org/10.1109/IEEM.2017.8290162

Renna, P. (2019). Adaptive policy of buffer allocation and preventive maintenance actions in unreliable production lines. Journal of Industrial Engineering International, 15(3), 411-421. https://doi.org/10.1007/s40092-018-0301-7

Shaaban, S., & Romero-Silva, R. (2020). Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs. Central European Journal of Operations Research. https://doi.org/10.1007/s10100-019-00670-9

Su, C., Shi, Y., & Dou, J. (2017). Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. Journal of Cleaner Production, 166, 756-770. https://doi.org/10.1016/j.jclepro.2017.08.064

Wang, G., Shin, Y. W., & Moon, D. H. (2016). Comparison of three flow line layouts with unreliable machines and profit maximization. Flexible Services and Manufacturing Journal, 28(4), 669-693. https://doi.org/10.1007/s10696-015-9233-3

Wang, G., Song, S., Shin, Y. W., & Moon, D. H. (2014). A simulation based study on increasing production capacity in a crankshaft line considering limited budget and space. Journal of Korean Institute of Industrial Engineers, 40(5), 481-491. https://doi.org/10.7232/JKIIE.2014.40.5.481

Weiss, S., Schwarz, J. A., & Stolletz, R. (2019). The buffer allocation problem in production lines: Formulations, solution methods, and instances. IISE Transactions, 51(5), 456-485. https://doi.org/10.1080/24725854.2018.1442031

Weiss, S., & Stolletz, R. (2015). Buffer allocation in stochastic flow lines via sample-based optimization with initial bounds. OR Spectrum, 37(4), 869-902. https://doi.org/10.1007/s00291-015-0393-z

Xi, S., Smith, J. M., Chen, Q., Mao, N., Zhang, H., & Yu, A. (2021). Simultaneous machine selection and buffer allocation in large unbalanced series-parallel production lines. International Journal of Production Research. https://doi.org/10.1080/00207543.2021.1884306

Yu, P. L. (1973). A class of solutions for group decision problems. Management Science, 19(8), 936-946. https://doi.org/10.1287/mnsc.19.8.936

Yuzukirmizi, M., & Smith, J. M. G. (2008). Optimal buffer allocation in finite closed networks with multiple servers. Computers and Operations Research, 35(8), 2579-2598. https://doi.org/10.1016/j.cor.2006.12.008

Zandieh, M., Joreir-Ahmadi, M. N., & Fadaei-Rafsanjani, A. (2017). Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines. International Journal of Advanced Manufacturing Technology, 91(5-8), 2581-2593. https://doi.org/10.1007/s00170-016-9744-4

Zhou, B. H., Liu, Y. W., Yu, J. Di, & Tao, D. (2018). Optimization of buffer allocation in unreliable production lines based on availability evaluation. Optimal Control Applications and Methods, 39(1), 204-219. https://doi.org/10.1002/oca.2341

[-]

recommendations

 

This item appears in the following Collection(s)

Show full item record