Management of task scheduling for production in machining workshops with MOEAs systems

Authors

  • Hiovanis Castillo Pantoja Universidad de Holguín, Cuba.
  • Lidia María Pérez Vallejo Universidad de Holguín, Cuba.
  • Roberto Pérez Rodríguez Universidad de Holguín, Cuba.

DOI:

https://doi.org/10.51896/rilco.v6i21.421

Keywords:

Management, scheduling, MOEAs, NSAG-III

Abstract

Currently, the management of productive programming in manufacturing companies determines the efficient use of resources and the timely delivery of finished products to customers. In this context, given the limitations in the logistics of raw materials, industrial plants adopt solutions based on multi-objective optimization for the management of production capacities. This work focuses on improving the efficiency of short-term production in working shops, using the variant of the multi-objective evolutionary method (MOEAs). Three variables are considered in the optimal multi-objective model: production cost, production volume and the surface finish of the parts. Four objectives are taken to minimize: speed of the abrasive tool, speed of the part, depth of cut and feed. As a case study, a grinding process was taken for the production of prismatic metal parts in a working shop. The quantitative evaluation of the management of the production schedule is shown and the effectiveness of the proposed method is demonstrated when making the comparison with two optimization methods.

References

Fu, G., Kapelan, Z., Kasprzyk, J. R., & Reed, P. (2013). Optimal design of water distribution systems using many-objective visual analytics. Journal of Water Resources Planning and Management, 139(6), 624-633. https://doi.org/doi:10.1061/(ASCE)WR.1943-5452.0000311

Huang, Y., Duan, H.-F., Zhao, M., Zhang, Q., Zhao, H., & Zhang, K. (2017). Probabilistic analysis and evaluation of nodal demand effect on transient analysis in urban water distribution systems. Journal of Water Resources Planning and Management, 143(8), 04017041. https://doi.org/doi:10.1061/(ASCE)WR.1943-5452.0000797

Kahhal, P., Brooghani, S. Y. A., & Azodi, H. D. (2013). Multi-objective optimization of sheet metal forming die using FEA coupled with RSM. Journal of Mechanical Science and Technology, 27(12), 3835-3842. https://doi.org/10.1007/s12206-013-0927-8

Patil, S. S., & Bhalerao, Y. J. (2019). Application of NSGA-II for optimisation of cylindrical plunge grinding process parameters. International Journal of Abrasive Technology, 9(4), 319-329. https://doi.org/10.1504/ijat.2019.106678

Sang, Y., & Tan, J. (2022). Intelligent factory many-objective distributed flexible job shop collaborative scheduling method. Computers & Industrial Engineering, 164, 107884. https://doi.org/https://doi.org/10.1016/j.cie.2021.107884

Xu, E. B., Yang, M. S., Li, Y., Gao, X. Q., Wang, Z. Y., & Ren, L. J. (2021). A multi-objective selective maintenance optimization method for series-parallel systems using NSGA-III and NSGA-II evolutionary algorithms. Advances in Production Engineering & Management, 16(3), 372-384. https://doi.org/https://doi.org/10.14743/apem2021.3.407

Wang, Y., Chen, C., Tao, Y., Wen, Z., Chen, B., & Zhang, H. (2019). A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry. Applied Energy, 242, 46-56. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.03.048

Wang, J., Tian, Y., Hu, X., Li, Y., Zhang, K., & Liu, Y. (2021). Predictive modelling and Pareto optimization for energy efficient grinding based on aANN-embedded NSGA II algorithm. Journal of Cleaner Production, 327, 129479. https://doi.org/https://doi.org/10.1016/j.jclepro.2021.129479

Zhang G., Liu M., Li J., Ming W. , Shao X. & Huang Y. (2014). Multi-objetive optimization for surface grinding process using a hybrid particle sward optimization algorithm. International Journal Advanced Manufacturing Technology, 71, 1861-1872. https://doi.org/https://doi.org/10.1007/s00170-013-5571-z

Published

2024-02-12

How to Cite

Castillo Pantoja, H., Pérez Vallejo, L. M., & Pérez Rodríguez, R. (2024). Management of task scheduling for production in machining workshops with MOEAs systems. Revista De Investigación Latinoamericana En Competitividad Organizacional, 6(21), 103–113. https://doi.org/10.51896/rilco.v6i21.421

Issue

Section

Estudio de Casos