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An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems

Author

Listed:
  • Hong-Yan Sang

    (Liaocheng University)

  • Quan-Ke Pan

    (Shanghai University)

  • Pei-Yong Duan

    (Liaocheng University)

  • Jun-Qing Li

    (Liaocheng University)

Abstract

Lot-streaming scheduling problem has been an active area of research due to its important applications in modern industries. This paper deals with the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion. An effective discrete invasive weed optimization (DIWO) algorithm is presented with new characteristics. A job permutation representation is utilized and an adapted Nawaz–Enscore–Ham heuristic is employed to ensure an initial weed colony with a certain level of quality. A new spatial dispersal model is designed based on the normal distribution and the property of tangent function to enhance global search. A local search procedure based on the insertion neighborhood is employed to perform local exploitation. The presented DIWO is calibrated by means of the design of experiments approach. A comparative evaluation is carried out with several best performing algorithms based on a total of 280 randomly generated instances. The numerical experiments show that the presented DIWO algorithm produces significantly better results than the competing algorithms and it constitutes a new state-of-the-art solution for the lot-streaming flowshop problem with sequence-dependent setup times with makespan criterion.

Suggested Citation

  • Hong-Yan Sang & Quan-Ke Pan & Pei-Yong Duan & Jun-Qing Li, 2018. "An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(6), pages 1337-1349, August.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:6:d:10.1007_s10845-015-1182-x
    DOI: 10.1007/s10845-015-1182-x
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    References listed on IDEAS

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    Cited by:

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    2. Wenjie Wang & Guangdong Tian & Gang Yuan & Duc Truong Pham, 2023. "Energy-time tradeoffs for remanufacturing system scheduling using an invasive weed optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1065-1083, March.
    3. Jiang Li & Lihong Guo & Yan Li & Chang Liu, 2019. "Enhancing Elephant Herding Optimization with Novel Individual Updating Strategies for Large-Scale Optimization Problems," Mathematics, MDPI, vol. 7(5), pages 1-35, April.
    4. Zheng, Zhi-xin & Li, Jun-qing & Duan, Pei-yong, 2019. "Optimal chiller loading by improved artificial fish swarm algorithm for energy saving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 227-243.
    5. Xiaoqi Zhao & Haipeng Qu & Wenjie Lv & Shuo Li & Jianliang Xu, 2021. "MooFuzz: Many-Objective Optimization Seed Schedule for Fuzzer," Mathematics, MDPI, vol. 9(3), pages 1-19, January.

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