Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming

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

@Article{Zhou:2020:IJPR,

• author = "Yong Zhou and Jian-jun Yang and Zhuang Huang",
• title = "Automatic design of scheduling policies for dynamic flexible job shop scheduling via surrogate-assisted cooperative co-evolution genetic programming",
• journal = "International Journal of Production Research",
• year = "2020",
• volume = "58",
• number = "9",
• pages = "2561--2580",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "0020-7543",
• bibsource = "OAI-PMH server at oai.repec.org",
• identifier = "RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2561-2580",
• oai = "oai:RePEc:taf:tprsxx:v:58:y:2020:i:9:p:2561-2580",
• DOI = "doi:10.1080/00207543.2019.1620362",
• abstract = "At present, a lot of references use discrete event simulation to evaluate the fitness of evolved rules, but which simulation configuration can achieve better evolutionary rules in a limited time has not been fully studied. This study proposes three types of hyper-heuristic methods for coevolution of the machine assignment rules (MAR) and job sequencing rules (JSR) to solve the DFJSP, including the cooperative coevolution genetic programming with two sub-populations (CCGP), the genetic programming with two sub-trees (TTGP) and the genetic expression programming with two sub-chromosomes (GEP). After careful parameter tuning, a surrogate simulation model is used to evaluate the fitness of evolved scheduling policies (SP). Computational simulations and comparisons demonstrate that the proposed surrogate-assisted CCGP method (CCGP-SM) shows competitive performance with other evolutionary approaches using the same computation time. Furthermore, the learning process of the proposed methods demonstrates that the surrogate-assisted GP methods help accelerating the evolutionary process and improving the quality of the evolved SPs without a significant increase in the length of SP. In addition, the evolved SPs generated by the CCGP-SM show superior performance as compared with existing rules in the literature. These results demonstrate the effectiveness and robustness of the proposed method.",

}

Genetic Programming entries for Yong Zhou Jian-Jun Yang Zhuang Huang

Citations