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Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 43

Knitwear Production Scheduling

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DOI: http://dx.doi.org/10.15439/2025F4025

Citation: Proceedings of the 20th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 43, pages 693–697 ()

Full text

Abstract. Clothing production is an important part of the industry. It includes the sewing, knitting and leather industries. It is important for a manufacturer to organize the production process well. This organization includes personnel allocation, machine loading, and material allocation. The goal is to complete the given order in the shortest time and, if possible, at the lowest cost. In this article, we look at the task of making knitted shirts. An ant colony optimization algorithm is proposed to solve the problem. The objective is to complete the order in the shortest possible time.

References

  1. Xiong H., Shi S., Ren D., Hu J.,A survey of job shop scheduling problem: The types and models, Computers & Operations Research, Vol. 142, 2022, 105731.
  2. Johnson S.M., Optimal Two- and Three-stage Production Schedules With Setup Times Included, Naval Res. Logist. Quart., Vol. 1(1), 1954, 61-68.
  3. Akers S.B., Friedman J., A Non-Numerical Approach to Production Scheduling Problems, J. Oper. Res. Soc. Am., Vol. 3(4), 1955, 429-442.
  4. Akers S.B., A Graphical Approach to Production Scheduling Problems, Oper. Res., Vol.4(2), 1956, 244-245.
  5. Sisson R.L., Methods of Sequencing in Job Shops- A Review, Oper. Res., Vol.7(1), 1959, 10-29.
  6. Jerald J., Asokan P., Saravanan R., Rani A.D.C., Simultaneous scheduling of parts and automated guided vehicles in an FMS environment using adaptive genetic algorithm, Int. J. Adv. Manuf. Technol., Vol.29(5–6), 2006, 584-589.
  7. Yin L., Li X., Gao L., Lu C., Zhang Z., A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem, Sustainable Comput. Inf. Syst., Vol.13, 2017, 15-30.
  8. Meng L., Zhang C., Shao X., Ren Y., MILP models for energy-aware flexible job shop scheduling problem, J. Cleaner Prod., Vol. 210, 2019, 710-723.
  9. Ham A., Flexible job shop scheduling problem with parallel batch processing machine, Proceedings of the 2016 Winter Simulation Conference, 2016, 2740-2749.
  10. Gao K.Z., Suganthan P.N., Pan Q.K., Tasgetiren M.P., A. Sadollah A., Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion, Knowl.-Based Syst., Vol. 109, 2016, 1-16.
  11. Liu Q., Zhan M., Chekem F.O., Shao X., Ying B., Sutherland J.M., A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint, J. Cleaner Prod., Vol. 168, 2017, 668-678.
  12. Yang Y.Z., Gu X.S., Pareto-based complete local search and combined timetabling for multi-objective job shop scheduling problem with no-wait constraint, J. Donghua Univ. (English Ed.), Vol. 33(4), 2016, 601-624.
  13. Wang X., Wang B., Zhang X., Xia X., Q. Pan Q., Two-objective robust job-shop scheduling with two problem-specific neighborhood structures, Swarm Evol. Comput., Vol. 61, 2021, 100805.
  14. Li K., Deng Q., Zhang L., Fan Q., Gong G., Ding S.,An effective MCTS-based algorithm for minimizing makespan in dynamic flexible job shop scheduling problem, Comput. Ind. Eng., Vol. 155(2), 2021, 107211.
  15. An Y., Chen X., Li Y., Han Y., Zhang J., Shi H., An improved non-dominated sorting biogeography-based optimization algorithm for the (hybrid) multi-objective flexible job-shop scheduling problem, Appl. Soft Comput.,Vol. 99, 2021, 106869.
  16. Anghinolfi D., Paolucci M., Ronco R., A bi-objective heuristic approach for green identical parallel machine scheduling, Eur. J. Oper. Res., Vol. 289(2), 2021, 416-434.
  17. Grosch B., Kohne T., Weigold M,, Multi-objective hybrid genetic algorithm for energy adaptive production scheduling in job shops, Procedia CIRP, Vol. 98, 2021, 294-299.
  18. Scalia B., Leite C.F., The Job-Shop Scheduling Problem: Mixed-Integer Programming Models, TCS Archive, 2023.
  19. Bynum, M. L. et al., Pyomo-optimization modeling in python, Springer, 2021.
  20. Dauzère-Pérès S., Ding J., Shen L., Tamssaouet K., The flexible job shop scheduling problem: A review, European Journal of Operational Research, Vol. 314(2), 2024, 409-432.
  21. Dorigo M, Stutzle T., Ant Colony Optimization, MIT Press, 2004.
  22. Fidanova S. Hybrid Ant Colony Optimization Algorithm for Multiple Knapsack Problem. 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), IEEE, 2021, DOI:10.1109/ICRAIE51050.2020.9358351, 1-5.
  23. Bonabeau E., Dorigo M. and Theraulaz G., Swarm Intelligence: From Natural to Artificial Systems, New York,Oxford University Press, 1999.