Automatic Generation of Energy-Efficient Dispatching Rules for Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Fei:2023:CAC,

• author = "Baolin Fei and Binzi Xu and Dengchao Huang and Yao Zhang and Chun Wang and Long Yang",
• booktitle = "2023 China Automation Congress (CAC)",
• title = "Automatic Generation of Energy-Efficient Dispatching Rules for Dynamic Flexible Job Shop Scheduling",
• year = "2023",
• pages = "533--538",
• abstract = "Dynamic flexible job shop scheduling (DFJSS) is an important and complex combinatorial optimisation problem. Heuristic methods have been extensively studied and proven to be effective in solving the job shop scheduling problems, but they still suffer from difficulties in real-time scheduling when dealing with dynamic environments. In comparison to heuristic methods, genetic programming hyper-heuristic (GPHH) is more suitable for tackling dynamic events since it can make real-time decisions by dispatching rules (DRs) automatically generated based on the current job shop state. However, most existing DR-based studies focus on time-related optimisation objectives (e.g., makespan, tardiness, etc.), ignoring energy consumption, which is crucial for meeting the urgent needs of green manufacturing in current society. Therefore, this paper systematically designs the energy-efficient terminals for GPHH, following an in-depth analysis of energy flow in the job shop. Besides, the paper proposes the energy-efficient manually designed DRs based on the DR construction method. Experimental results demonstrate that the DRs containing the proposed energy-efficient terminals can effectively optimise energy-related objectives.",
• keywords = "genetic algorithms, genetic programming, Energy consumption, Green manufacturing, Job shop scheduling, Heuristic algorithms, Dynamic scheduling, Energy efficiency, dynamic flexible job shop scheduling, GPHH, dispatching rules, energy consumption",
• DOI = "doi:10.1109/CAC59555.2023.10451974",
• ISSN = "2688-0938",
• month = nov,
• notes = "Also known as \cite{10451974}",

}

Genetic Programming entries for Baolin Fei Binzi Xu Dengchao Huang Yao Zhang Chun Wang Long Yang

Citations