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A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop

Author

Listed:
  • Jiansha Lu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Lili Xu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jinghao Jin

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yiping Shao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

The integrated scheduling problem in automated storage and retrieval systems (AS/RS) and the hybrid flowshop is critical for the realization of lean logistics and just-in-time distribution in manufacturing systems. The bi-objective model that minimizes the operation time in AS/RS and the makespan in the hybrid flowshop is established to optimize the problem. A mixed algorithm, named GA-MBO algorithm, is proposed to solve the model, which combines the advantages of the strong global optimization ability of genetic algorithm (GA) and the strong local search ability of migratory birds optimization (MBO). To avoid useless solutions, different cross operations of storage and retrieval tasks are designed. Compared with three algorithms, including improved genetic algorithm, improved particle swam optimization, and a hybrid algorithm of GA and particle swam optimization, the experimental results showed that the GA-MBO algorithm improves the operation efficiency by 9.48%, 19.54%, and 5.12% and the algorithm robustness by 35.16%, 54.42%, and 39.38%, respectively, which further verified the effectiveness of the proposed algorithm. The comparative analysis of the bi-objective experimental results fully reflects the superiority of integrated scheduling optimization.

Suggested Citation

  • Jiansha Lu & Lili Xu & Jinghao Jin & Yiping Shao, 2022. "A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop," Energies, MDPI, vol. 15(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7558-:d:941347
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    References listed on IDEAS

    as
    1. Wen-Jie Xu & Li-Jun He & Guang-Yu Zhu, 2021. "Many-objective flow shop scheduling optimisation with genetic algorithm based on fuzzy sets," International Journal of Production Research, Taylor & Francis Journals, vol. 59(3), pages 702-726, February.
    2. Yubo Song & Haibo Mu & Muazzam Maqsood, 2022. "Integrated Optimization of Input/Output Point Assignment and Twin Stackers Scheduling in Multi-Input/Output Points Automated Storage and Retrieval System by Ant Colony Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-18, May.
    3. Kuo-Yang Wu & Sendren Sheng-Dong Xu & Tzong-Chen Wu, 2013. "Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms," Abstract and Applied Analysis, Hindawi, vol. 2013, pages 1-17, July.
    4. Yingli Li & Xinyu Li & Liang Gao & Biao Zhang & Quan-Ke Pan & M. Fatih Tasgetiren & Leilei Meng, 2021. "A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times," International Journal of Production Research, Taylor & Francis Journals, vol. 59(13), pages 3880-3899, July.
    5. Edgar Ramos Muñoz & Faryar Jabbari, 2022. "An Octopus Charger-Based Smart Protocol for Battery Electric Vehicle Charging at a Workplace Parking Structure," Energies, MDPI, vol. 15(17), pages 1-25, September.
    6. Piotr Sawicki & Hanna Sawicka, 2021. "Optimisation of the Two-Tier Distribution System in Omni-Channel Environment," Energies, MDPI, vol. 14(22), pages 1-22, November.
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