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Modeling and Optimization of Cable Production Scheduling by Incorporating an Ant Colony Algorithm

Author

Listed:
  • Changbiao Zhu

    (School of Mechanical Engineering, Southeast University, Nanjing 211189, China
    Anhui Cable Co., Ltd., Tianchang 239300, China)

  • Chongxin Wang

    (School of Mechanical Engineering, Southeast University, Nanjing 211189, China)

  • Zhonghua Ni

    (School of Mechanical Engineering, Southeast University, Nanjing 211189, China)

  • Xiaojun Liu

    (School of Mechanical Engineering, Southeast University, Nanjing 211189, China
    Engineering Research Center of New Light Sources Technology and Equipment, Ministry of Education, Southeast University, Nanjing 210018, China)

  • Abbas Raza

    (School of Mechanical Engineering, Southeast University, Nanjing 211189, China)

Abstract

With the development of small batch and multi-batch service production mode, manual scheduling by hand has been difficult to adapt to the production of a large number of complex orders. This work proposed a cable production scheduling optimization method based on an ant colony algorithm, aiming at solving the problems of the inefficiency and underutilization of resources in the process of traditional cable scheduling. Applying an ant colony (ACO) algorithm to solve the production scheduling problem achieved the intelligent scheduling and optimization of production tasks. The method utilizes the search and optimization capabilities of the ant colony algorithm, with the characteristics of the cable production line, achieving a reasonable allocation and scheduling of production tasks. After applying the proposed model to the cable production line, the scheduling scheme generated by the ACO algorithm-based objective order scheduling method reduced the total production time required from 3 days to 2.6882 days, resulting in a 10.04% increase in production efficiency. The results show that the method can effectively improve the production efficiency and resource utilization of the cable production line, and has high practicality and feasibility.

Suggested Citation

  • Changbiao Zhu & Chongxin Wang & Zhonghua Ni & Xiaojun Liu & Abbas Raza, 2025. "Modeling and Optimization of Cable Production Scheduling by Incorporating an Ant Colony Algorithm," Mathematics, MDPI, vol. 13(8), pages 1-26, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1235-:d:1631103
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    References listed on IDEAS

    as
    1. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
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