CLC number: TP278
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2010-09-03
Cited: 2
Clicked: 8085
Jing Chen, Shu-you Zhang, Zhan Gao, Li-xin Yang. Feature-based initial population generation for the optimization of job shop problems[J]. Journal of Zhejiang University Science C, 2010, 11(10): 767-777.
@article{title="Feature-based initial population generation for the optimization of job shop problems",
author="Jing Chen, Shu-you Zhang, Zhan Gao, Li-xin Yang",
journal="Journal of Zhejiang University Science C",
volume="11",
number="10",
pages="767-777",
year="2010",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C0910707"
}
%0 Journal Article
%T Feature-based initial population generation for the optimization of job shop problems
%A Jing Chen
%A Shu-you Zhang
%A Zhan Gao
%A Li-xin Yang
%J Journal of Zhejiang University SCIENCE C
%V 11
%N 10
%P 767-777
%@ 1869-1951
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C0910707
TY - JOUR
T1 - Feature-based initial population generation for the optimization of job shop problems
A1 - Jing Chen
A1 - Shu-you Zhang
A1 - Zhan Gao
A1 - Li-xin Yang
J0 - Journal of Zhejiang University Science C
VL - 11
IS - 10
SP - 767
EP - 777
%@ 1869-1951
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C0910707
Abstract: A suitable initial value of a good (close to the optimal value) scheduling algorithm may greatly speed up the convergence rate. However, the initial population of current scheduling algorithms is randomly determined. Similar scheduling instances in the production process are not reused rationally. For this reason, we propose a method to generate the initial population of job shop problems. The scheduling model includes static and dynamic knowledge to generate the initial population of the genetic algorithm. The knowledge reflects scheduling constraints and priority rules. A scheduling strategy is implemented by matching and combining the two categories of scheduling knowledge, while the experience of dispatchers is externalized to semantic features. Feature similarity based knowledge matching is utilized to acquire the constraints that are in turn used to optimize the scheduling process. Results show that the proposed approach is feasible and effective for the job shop optimization problem.
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