JACIII Vol.26 No.6 pp. 974-982
doi: 10.20965/jaciii.2022.p0974


Memetic Algorithm for Dynamic Joint Flexible Job Shop Scheduling with Machines and Transportation Robots

Yingmei He*, Bin Xin*,†, Sai Lu*, Qing Wang*, and Yulong Ding**

*School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 10081, China

**Peng Cheng Laboratory
Shenzhen 518055, China

Corresponding author

March 19, 2022
July 6, 2022
November 20, 2022
flexible job shop, dynamic joint scheduling, battery charging maintenance, multi-agent
Memetic Algorithm for Dynamic Joint Flexible Job Shop Scheduling with Machines and Transportation Robots

Dynamic joint job shop scheduling

In this study, the dynamic joint scheduling problem for processing machines and transportation robots in a flexible job shop is investigated. The study aims to minimize the order completion time (makespan) of a job shop manufacturing system. Considering breakdowns, order insertion and battery charging maintenance of robots, an event-driven global rescheduling strategy is adopted. A novel memetic algorithm combining genetic algorithm and variable neighborhood search is designed to handle dynamic events and obtain a new scheduling plan. Finally, numerical experiments are conducted to test the effect of the improved operators. For successive multiple rescheduling, the effectiveness of the proposed algorithm is verified by comparing it with three other algorithms under dynamic events, and through statistical analysis, the results verify the effectiveness of the proposed algorithm.

Cite this article as:
Y. He, B. Xin, S. Lu, Q. Wang, and Y. Ding, “Memetic Algorithm for Dynamic Joint Flexible Job Shop Scheduling with Machines and Transportation Robots,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.6, pp. 974-982, 2022.
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Last updated on Dec. 01, 2022