A Genetic Programming-Based Evolutionary Approach for Flexible Job Shop Scheduling with Multiple Process Plans

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Zhu:2020:CASE,

• author = "Xuedong Zhu and Weihao Wang and Xinxing Guo and Leyuan Shi",
• title = "A Genetic Programming-Based Evolutionary Approach for Flexible Job Shop Scheduling with Multiple Process Plans",
• booktitle = "2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)",
• year = "2020",
• pages = "49--54",
• abstract = "This paper investigates a more general flexible job shop scheduling problem with multiple process plans which is common in the modern manufacturing system. As an extension of the traditional flexible job shop scheduling problem, various realistic flexibility such as processing flexibility, machine flexibility and sequencing flexibility are considered in this problem. Due to the high complexity and the real-time requirement of this problem, a genetic programming-based evolutionary approach is proposed to automatically generate effective dispatching rules for this problem, and an evaluation method is developed to evaluate the generated dispatching rules. Three experiments are conducted to evaluate the performance of the proposed approach for real cases with large-scale test problems. Numerical results show that the proposed approach outperforms the classical dispatching rules and the state-of-theart algorithms, and is able to provide higher-quality solutions with less computational time.",
• keywords = "genetic algorithms, genetic programming, Dispatching, Sequential analysis, Job shop scheduling, Manufacturing systems, Conferences",
• DOI = "doi:10.1109/CASE48305.2020.9216783",
• ISSN = "2161-8089",
• month = aug,
• notes = "Also known as \cite{9216783}",

}

Genetic Programming entries for Xuedong Zhu Weihao Wang Xinxing Guo Leyuan Shi

Citations