An application of genetic algorithm into order scheduling of a textile company

Các tác giả

  • Lê Đức Hạnh Trường Đại học Bách khoa - Đại học Quốc gia TP.HCM
  • Lê Đức Đạo Trường Đại học Bách khoa - Đại học Quốc gia TP.HCM
  • Ngô Xuân Minh Trường Đại học Bách khoa - Đại học Quốc gia TP.HCM
DOI: https://doi.org/10.59294/HIUJS.VOL.6.2024.634

Từ khóa:

Genetic Algorithm, resource utilization, production efficiency, machine in parallel, dye scheduling, total completion time

Abstract

Scheduling not only poses a significant complexity problem in manufacturing inside each of enterprise but extending to the competitive landscape among enterprises as well. The footwear industry, particularly in the field of accessories for shoe production, is no exception to this challenge. This research paper addresses scheduling issues that arising in the dyeing plant, for the product of shoelaces with various restrictions such as color, machines resource and the demand uncertainty. By emerged the Genetic Algorithm (GA) as modeling evolutionary system, incorporating the two-point crossover, swap mutation operators, and k-tournament selection, which are the operators that mainly used in the job shop and flow shop environment, to the scheduling of machine in parallel environment. The purpose of such the application is to find the efficient allocation of jobs, focus on addressing order lateness by minimizing the total completion time of all batches. The optimization results demonstrate the schedule of a one-day scenario production, providing practical insights into the benefits of GA when comparing to the MILP and current scheduling method of the company. Overall, the research contributes valuable findings to the field of textile dyeing scheduling, offering a robust solution to enhance production efficiency, resource utilization, and order sequencing in the footwear auxiliary enterprise.

Tài liệu tham khảo

[1] Z. I. M. Hassani, A. E. Barkany, A. M. Darcherif, A. Jabri, and I. E. Abbassi, “Planning and scheduling problems of production systems: review, classification and opportunities,” International Journal of Productivity and Quality Management, vol. 28, no. 3, p. 372, 2019.

DOI: https://doi.org/10.1504/IJPQM.2019.103520

[2] Q. Zhang et al., "A Continuous Time Scheduling Model for Printing and Dyeing Plants," in Proc. 2nd Int. Conf. Mathematics, Modeling and Simulation Technologies and Applications (MMSTA 2019), Atlantis Press, pp. 16-20, 2019.

DOI: https://doi.org/10.2991/mmsta-19.2019.4

[3] J. M. Pinto, I. E. Grossmann, and E. C. Research, "A continuous time mixed integer linear programming model for short term scheduling of multistage batch plants," Comput. Chem. Eng., vol. 34, no. 9, pp. 3037-3051, 1995.

DOI: https://doi.org/10.1021/ie00048a015

[4] J. Cerdá, G. P. Henning, and I. E. Grossmann, "A mixed-integer linear programming model for short-term scheduling of single-stage multiproduct batch plants with parallel lines," Comput. Chem. Eng., vol. 36, no. 5, pp. 1695-1707, 1997.

DOI: https://doi.org/10.1021/ie9605490

[5] E. K. Burke and G. Kendall, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques. Springer, 2014.

DOI: https://doi.org/10.1007/978-1-4614-6940-7

[6] N.-T. Huynh, C.-F. Chien, "A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study," Computers & Industrial Engineering, vol. 125, pp. 615-627, 2018.

DOI: https://doi.org/10.1016/j.cie.2018.01.005

[7] H. Kurniawan, T. D. Sofianti, and A. T. Pratama, "Optimizing Production Scheduling Using Genetic Algorithm Case Study in PT. Kurnia Ratu Kencana," Doctoral dissertation, Swiss German University, 2014.

[8] T. Harada and E. Alba, "Parallel genetic algorithms: a useful survey," Appl. Soft Comput., vol. 53, pp. 1-39, 2020.

DOI: https://doi.org/10.1145/3400031

[9] G. Weiss, "Scheduling: Theory, Algorithms, and Systems," JSTOR, 1995.

[10] R. Ehtesham Rasi and M. Sohanian, "A multi-objective optimization model for sustainable supply chain network with using genetic algorithm," J. Modell. Manage., vol. 16, no. 2, pp. 714-727, 2021.

DOI: https://doi.org/10.1108/JM2-06-2020-0150

[11] J. Józefowska and A. Zimniak, “Optimization tool for short-term production planning and scheduling,” International Journal of Production Economics, vol. 112, no. 1, pp. 109–120, Mar. 2008,

DOI: https://doi.org/10.1016/j.ijpe.2006.08.026

[12] M. A. Albadr et al., "Genetic algorithm based on natural selection theory for optimization problems," Scientific Reports, vol. 12, no. 11, p. 1758, 2020.

DOI: https://doi.org/10.3390/sym12111758

[13] E. G. Shopova and N. G. Vaklieva-Bancheva, "BASIC—A genetic algorithm for engineering problems solution," Chem. Eng. Commun., vol. 30, no. 8, pp. 1293-1309, 2006.

DOI: https://doi.org/10.1016/j.compchemeng.2006.03.003

[14] S. S. Alves, S. A. Oliveira, and A. R. R. Neto, "A novel educational timetabling solution through recursive genetic algorithms," in 2015 Latin America Congress on Computational Intelligence (LA-CCI), IEEE, pp. 1-6, 2015.

DOI: https://doi.org/10.1109/LA-CCI.2015.7435955

[15] H. Chen, J. Ihlow, and C. Lehmann, "A genetic algorithm for flexible job-shop scheduling," in Proc. 1999 IEEE Int. Conf. Robotics and Automation, vol. 2, IEEE, pp. 1120-1125, 1999.

Tải xuống

Số lượt xem: 536
Tải xuống: 26

Đã xuất bản

24.06.2024

Cách trích dẫn

[1]
L. Đức H. Lê Đức Hạnh, L. Đức Đạo Lê Đức Đạo, và N. X. M. Ngô Xuân Minh, “An application of genetic algorithm into order scheduling of a textile company”, HIUJS, vol 6, tr 91–98, tháng 6 2024.

Số

Chuyên mục

ECONOMICS AND MANAGEMENT SCIENCES