Optimization of the Master Production Scheduling in a Textile Industry Using Genetic Algorithm

  1. Lorente-Leyva, Leandro L. 1
  2. Murillo-Valle, Jefferson R. 1
  3. Yakcleem Montero-Santos 1
  4. Herrera-Granda, Israel D. 1
  5. Herrera-Granda, Erick P. 1
  6. Rosero-Montalvo, Paul D. 1
  7. Peluffo-Ordóñez, Diego H. 23
  8. Blanco-Valencia, Xiomara P. 3
  1. 1 Universidad Técnica del Norte
    info

    Universidad Técnica del Norte

    Ibarra, Ecuador

    ROR https://ror.org/03f0t8b71

  2. 2 Universidad Yachay Tech
    info

    Universidad Yachay Tech

    Urcuquí, Ecuador

    ROR https://ror.org/04jjswc10

  3. 3 SDAS Research Group (Ibarra, Ecuador)
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Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 674-685

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

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Resumen

In a competitive environment, an industry’s success is directly related to the level of optimization of its processes, how production is planned and developed. In this area, the master production scheduling (MPS) is the key action for success. The object of study arises from the need to optimize the medium-term production planning system in a textile company, through genetic algorithms. This research begins with the analysis of the constraints, mainly determined by the installed capacity and the number of workers. The aggregate production planning is carried out for the T-shirts families. Due to such complexity, the application of bioinspired optimization techniques demonstrates their best performance, before industries that normally employ exact and simple methods that provide an empirical MPS but can compromise efficiency and costs. The products are then disaggregated for each of the items in which the MPS is determined, based on the analysis of the demand forecast, and the orders made by customers. From this, with the use of genetic algorithms, the MPS is optimized to carry out production planning, with an improvement of up to 96% of the level of service provided.

Fuente de los datos: Dialnet