Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling

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

@Article{sitahong:2023:Processes,

• author = "Adilanmu Sitahong and Yiping Yuan and Ming Li and Junyan Ma and Zhiyong Ba and Yongxin Lu",
• title = "Designing Dispatching Rules via Novel Genetic Programming with Feature Selection in Dynamic Job-Shop Scheduling",
• journal = "Processes",
• year = "2023",
• volume = "11",
• number = "1",
• pages = "Article No. 65",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2227-9717",
• URL = "https://www.mdpi.com/2227-9717/11/1/65",
• DOI = "doi:10.3390/pr11010065",
• abstract = "Genetic Programming (GP) has been widely employed to create dispatching rules intelligently for production scheduling. The success of GP depends on a suitable terminal set of selected features. Specifically, techniques that consider feature selection in GP to enhance rule understandability for dynamic job shop scheduling (DJSS) have been successful. However, existing feature selection algorithms in GP focus more emphasis on obtaining more compact rules with fewer features than on improving effectiveness. This paper is an attempt at combining a novel GP method, GP via dynamic diversity management, with feature selection to design effective and interpretable dispatching rules for DJSS. The idea of the novel GP method is to achieve a progressive transition from exploration to exploitation by relating the level of population diversity to the stopping criteria and elapsed duration. We hypothesize that diverse and promising individuals obtained from the novel GP method can guide the feature selection to design competitive rules. The proposed approach is compared with three GP-based algorithms and 20 benchmark rules in the different job shop conditions and scheduling objectives. Experiments show that the proposed approach greatly outperforms the compared methods in generating more interpretable and effective rules for the three objective functions. Overall, the average improvement over the best-evolved rules by the other three GP-based algorithms is 13.28percent, 12.57percent, and 15.62percent in the mean tardiness (MT), mean flow time (MFT), and mean weighted tardiness (MWT) objective, respectively.",
• notes = "also known as \cite{pr11010065}",

}

Genetic Programming entries for Adilanmu Sitahong Yiping Yuan Ming Li Junyan Ma Zhiyong Ba Yongxin Lu

Citations