A Simulation Optimization-Aided Learning Method for Design Automation of Scheduling Rules

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Ma:2022:CASE,

• author = "Hang Ma and Cheng Zhang and Zhongshun Shi",
• booktitle = "2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)",
• title = "A Simulation Optimization-Aided Learning Method for Design Automation of Scheduling Rules",
• year = "2022",
• pages = "1992--1997",
• abstract = "Intelligent manufacturing systems require real-time optimization algorithms for daily operations management. Scheduling rules have been proven to be efficient and commonly used in plenty of practical production scenarios, especially for the large-scale problems. However, almost all the scheduling rules are manually designed, which is time consuming and also results in the large loss of accuracy for complex problems. This paper proposes a new simulation optimization-aided learning method, denoted by SOaL, for design automation of scheduling rules. The proposed SOaL method treats the automated design of scheduling rules as a simulation optimization problem, where we use genetic programming algorithm to guide the rule generation and introduce ranking and selection algorithm to improve the rule evaluation accuracy. Using dynamic job shop scheduling problem as the simulation testbed, numerical results show the superiority of the proposed method.",
• keywords = "genetic algorithms, genetic programming, Learning systems, Job shop scheduling, Design automation, Machine learning algorithms, Heuristic algorithms, Production",
• DOI = "doi:10.1109/CASE49997.2022.9926615",
• ISSN = "2161-8089",
• month = aug,
• notes = "Also known as \cite{9926615}",

}

Genetic Programming entries for Hang Ma Cheng Zhang Tony Zhongshun Shi

Citations