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

  • Leandro L. Lorente-LeyvaEmail author
  • Jefferson R. Murillo-Valle
  • Yakcleem Montero-Santos
  • Israel D. Herrera-Granda
  • Erick P. Herrera-Granda
  • Paul D. Rosero-Montalvo
  • Diego H. Peluffo-Ordóñez
  • Xiomara P. Blanco-Valencia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11734)


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.


Master Production Scheduling Optimization Textile industry Genetic algorithm Production planning Forecasting 



The authors acknowledge to the research project “Modelo para la optimización del Master Production Scheduling en entornos inciertos aplicando técnicas metaheurísticas” supported by Agreement HCD Nro. UTN-FICA-2017-0640 by Facultad de Ingeniería en Ciencias Aplicadas from Universidad Técnica del Norte. As well, authors thank the valuable support given by the SDAS Research Group (


  1. 1.
    Higgins, P., Browne, J.: Master production scheduling: a concurrent planning approach. Prod. Plan. Control 3(1), 2–18 (1992)CrossRefGoogle Scholar
  2. 2.
    Slack, N., Chambers, S., Johnston, R.: Operations Management, 4th edn. Pearson, Upper Saddle River (2004)Google Scholar
  3. 3.
    Wu, Z., Zhang, C., Zhu, X.: An ant colony algorithm for Master production scheduling optimization. In: Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (ISCAS), pp. 775–779 (2012).
  4. 4.
    Díaz-Madroñero, M., Mula, J., Peidro, D.: A review of discrete-time optimization models for tactical production planning. Int. J. Prod. Res. 52(17), 5171–5207 (2014). Scholar
  5. 5.
    Golmohammadi, D.: A study of scheduling under the theory of constraints. Int. J. Prod. Econ. 165, 38–50 (2015)., Art. no. 6034CrossRefGoogle Scholar
  6. 6.
    Jonsson, P., Kjellsdotter Ivert, L.: Improving performance with sophisticated master production scheduling. Int. J. Prod. Econ. 168, 118–130 (2015). Scholar
  7. 7.
    Korbaa, O., Yim, P., Gentina, J-C.: Solving transient scheduling problem for cyclic production using timed Petri nets and constraint programming. In: European Control Conference, ECC 1999 - Conference Proceedings, pp. 3938–3945 (2015)., Art. no. 7099947
  8. 8.
    Akhoondi, F., Lotfi, M.M.: A heuristic algorithm for master production scheduling problem with controllable processing times and scenario-based demands. Int. J. Prod. Res. 54(12), 3659–3676 (2016). Scholar
  9. 9.
    Alba, E., Luque, G., Nesmachnow, S.: Parallel metaheuristics: recent advances and new trends. Int. Trans. Oper. Res. 20, 1–48 (2013)CrossRefGoogle Scholar
  10. 10.
    Abedini, A., Li, W., Ye, H.: An optimization model for operating room scheduling to reduce blocking across the perioperative process. Procedia Manufact. 10, 60–70 (2017). Scholar
  11. 11.
    Abu, M., Abbas, I., AlSattar, H., Khaddar, A-G., Atiya, B.: Solution for multi-objective optimisation master production scheduling problems based on swarm intelligence algorithms. J. Comput. Theor. Nanosci. 14(11), 5184–5194 (2017). Scholar
  12. 12.
    Lorente, L., et al.: Applying lean manufacturing in the production process of rolling doors: a case study. J. Eng. Appl. Sci. 13(7), 1774–1781 (2018). Scholar
  13. 13.
    Soares, M., Vieira, G.: A new multi-objective optimization method for master production scheduling problems based on genetic algorithm. Int. J. Adv. Manuf. Technol. 41, 549–567 (2009). Scholar
  14. 14.
    Lorente-Leyva, L.L., et al.: Developments on solutions of the normalized-cut-clustering problem without eigenvectors. In: Huang, T., Lv, J., Sun, C., Tuzikov, Alexander V. (eds.) ISNN 2018. LNCS, vol. 10878, pp. 318–328. Springer, Cham (2018). Scholar
  15. 15.
    Luo, T., Li, G., Yu, N.: Research on manufacturing productivity based on improved genetic algorithms under internet information technology. Concurrency Comput. 31(10), e4859 (2019). Scholar
  16. 16.
    Pinto, A.R.F., Nagano, M.S.: An approach for the solution to order batching and sequencing in picking systems. Prod. Eng. Res. Devel. 13(3–4), 325–341 (2019). Scholar
  17. 17.
    Goli, A., Tirkolaee, E.B., Malmir, B., Bian, G.B., Sangaiah, A.K.: A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand. Computing 101(6), 499–529 (2019). Scholar
  18. 18.
    Lin, Y.K., Chang, P.C., Yeng, L.C.L., Huang, S.F.: Bi-objective optimization for a multistate job-shop production network using NSGA-II and TOPSIS. J. Manufact. Syst. 52, 43–54 (2019). Scholar
  19. 19.
    Ben-Ammar, O., Bettayeb, B., Dolgui, A.: Optimization of multi-period supply planning under stochastic lead times and a dynamic demand. Int. J. Prod. Econ. 218, 106–117 (2019). Scholar
  20. 20.
    Ribas, P.C.: Análise do uso de têmpera simulada na otimização do planejamento mestre da produção. Pontifícia Universidade Católica de Paraná, Curitiba (2003)Google Scholar
  21. 21.
    Wang, B., Guan, Z., Ullah, S., Xu, X., He, Z.: Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm. J. Intell. Manuf. 28(2), 419–436 (2017). Scholar
  22. 22.
    Muñoz, E., Capón-García, E., Muñoz, M., Montoya, P.: Decision-support platform for industrial recipe management. In: Mejia, J., Muñoz, M., Rocha, Á., Quiñonez, Y., Calvo-Manzano, J. (eds.) CIMPS 2017, vol. 688, pp. 198–206. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Leandro L. Lorente-Leyva
    • 1
    Email author
  • Jefferson R. Murillo-Valle
    • 1
  • Yakcleem Montero-Santos
    • 1
  • Israel D. Herrera-Granda
    • 1
  • Erick P. Herrera-Granda
    • 1
  • Paul D. Rosero-Montalvo
    • 1
  • Diego H. Peluffo-Ordóñez
    • 2
    • 3
  • Xiomara P. Blanco-Valencia
    • 3
  1. 1.Facultad de Ingeniería en Ciencias AplicadasUniversidad Técnica del NorteIbarraEcuador
  2. 2.Escuela de Ciencias Matemáticas y Tecnología InformáticaYachay TechSan Miguel de UrcuquíEcuador
  3. 3.SDAS Research GroupIbarraEcuador

Personalised recommendations