IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i20p7558-d941347.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/20/7558/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/20/7558/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimo Bertolini & Francesco Leali & Davide Mezzogori & Cristina Renzi, 2023. "A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    2. Hsien-Pin Hsu & Chia-Nan Wang & Thanh-Tuan Dang, 2022. "Simulation-Based Optimization Approaches for Dealing with Dual-Command Crane Scheduling Problem in Unit-Load Double-Deep AS/RS Considering Energy Consumption," Mathematics, MDPI, vol. 10(21), pages 1-30, October.
    3. Chenyao Zhang & Yuyan Han & Yuting Wang & Junqing Li & Kaizhou Gao, 2023. "A Distributed Blocking Flowshop Scheduling with Setup Times Using Multi-Factory Collaboration Iterated Greedy Algorithm," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
    4. Juliana Castaneda & Xabier A. Martin & Majsa Ammouriova & Javier Panadero & Angel A. Juan, 2022. "A Fuzzy Simheuristic for the Permutation Flow Shop Problem under Stochastic and Fuzzy Uncertainty," Mathematics, MDPI, vol. 10(10), pages 1-17, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7558-:d:941347. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.