A Simulation Optimization-Aided Learning Method for Design Automation of Scheduling Rules
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- @InProceedings{Ma:2022:CASE,
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author = "Hang Ma and Cheng Zhang and Zhongshun Shi",
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booktitle = "2022 IEEE 18th International Conference on Automation
Science and Engineering (CASE)",
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title = "A Simulation Optimization-Aided Learning Method for
Design Automation of Scheduling Rules",
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year = "2022",
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pages = "1992--1997",
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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.",
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keywords = "genetic algorithms, genetic programming, Learning
systems, Job shop scheduling, Design automation,
Machine learning algorithms, Heuristic algorithms,
Production",
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DOI = "doi:10.1109/CASE49997.2022.9926615",
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ISSN = "2161-8089",
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month = aug,
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notes = "Also known as \cite{9926615}",
- }
Genetic Programming entries for
Hang Ma
Cheng Zhang
Tony Zhongshun Shi
Citations