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Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming
DOI:10.1080/00207543.2018.1543964.png)
摘要
En 中文
In this paper, two new approaches are proposed for extracting composite priority rules for scheduling problems. The suggested approaches use simulation and gene expression programming and are able to evolve specific priority rules for all dynamic scheduling problems in accordance with their features. The methods are based on the idea that both the proper design of the function and terminal sets and the structure of the gene expression programming approach significantly affect the results. In the first proposed approach, modified and operational features of the scheduling environment are added to the terminal set, and a multigenic system is used, whereas in the second approach, priority rules are used as automatically defined functions, which are combined with the cellular system for gene expression programming. A comparison shows that the second approach generates better results than the first; however, all of the extracted rules yield better results than the rules from the literature, especially for the defined multi-objective function consisting of makespan, mean lateness and mean flow time. The presented methods and the generated priority rules are robust and can be applied to all real and large-scale dynamic scheduling problems.
Keyword:
dynamic job shop scheduling
priority rules
simulation
gene expression programming
multi-objective optimisation
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期刊
IF:
7.3
论文数:
1.1W
被引数:
3.7W
机构
引用论文
Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops
ENERGY
IF9.4
A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions在不同的流程时间和迟到相关目标函数下,作业车间调度问题的优先级规则的比较
Dynamic adjustment of dispatching rule parameters in flow shops with sequence-dependent set-up times

