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A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand

Author

Listed:
  • Zhi Li

    (Guangdong University of Technology)

  • Ali Vatankhah Barenji

    (Guangdong University of Technology
    Kennesaw State University)

  • Jiazhi Jiang

    (Guangdong University of Technology)

  • Ray Y. Zhong

    (The University of Hong Kong)

  • Gangyan Xu

    (Nanyang Technological University)

Abstract

Given the evolutionary journey of E-commerce, there have been emerging challenges confronting warehouse logistics, including smaller shipping units, more varieties and batches, and shorter cycles. These challenges are difficult to cope when using conventional scheduling with the robotic approach. Currently, automated storage and retrieval system are becoming preferred for warehouse companies with the help of mobile robots. However, when many orders are received simultaneously, the existing scheduling approach might make unreasonable decisions, leading to delayed packaging of entire orders and reducing the performance of the warehouse. Therefore, this paper addresses this problem and proposes a novel scheduling mechanism for multi-robot and tasks allocation problems which may arise in an intelligent warehouse system. This mechanism proposes into the intelligent warehouse troubled with simultaneous multiple customer demands. The mathematical model for the system is developed by considering a multitask robot facing dynamic customer demand. The proposed model’s approach is based on the particle swarm optimization heuristic. The result for this approach then compared with the genetic algorithm (GA). The simulation results demonstrate that the proposed solution is far superior to that of the GA for multi-robot scheduling and tasks allocation problems in the intelligent warehouse.

Suggested Citation

  • Zhi Li & Ali Vatankhah Barenji & Jiazhi Jiang & Ray Y. Zhong & Gangyan Xu, 2020. "A mechanism for scheduling multi robot intelligent warehouse system face with dynamic demand," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 469-480, February.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1459-y
    DOI: 10.1007/s10845-018-1459-y
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    References listed on IDEAS

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    Cited by:

    1. Dalila B. M. M. Fontes & S. Mahdi Homayouni & Mauricio G. C. Resende, 2022. "Job-shop scheduling-joint consideration of production, transport, and storage/retrieval systems," Journal of Combinatorial Optimization, Springer, vol. 44(2), pages 1284-1322, September.
    2. Janusz Szpytko & Yorlandys Salgado Duarte, 2021. "A digital twins concept model for integrated maintenance: a case study for crane operation," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1863-1881, October.
    3. R. Micale & C. M. La Fata & M. Enea & G. La Scalia, 2021. "Regenerative scheduling problem in engineer to order manufacturing: an economic assessment," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1913-1925, October.
    4. Seokgi Lee & Hyun Woo Jeon & Mona Issabakhsh & Ahmad Ebrahimi, 2022. "An electric forklift routing problem with battery charging and energy penalty constraints," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1761-1777, August.

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