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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (10): 767-777    DOI: 10.1631/jzus.C0910707
    
Feature-based initial population generation for the optimization of job shop problems
Jing Chen*,1, Shu-you Zhang1, Zhan Gao1, Li-xin Yang2
1 State Key Lab of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China 2 Top Vocational Institute of Information & Technology of Shaoxing, Shaoxing 312000, China
Feature-based initial population generation for the optimization of job shop problems
Jing Chen*,1, Shu-you Zhang1, Zhan Gao1, Li-xin Yang2
1 State Key Lab of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, China 2 Top Vocational Institute of Information & Technology of Shaoxing, Shaoxing 312000, China
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摘要： 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.
关键词： Scheduling feature;  Job shop problem (JSP);  Scheduling optimization;  Scheduling knowledge    
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.
Key words: Scheduling feature    Job shop problem (JSP)    Scheduling optimization    Scheduling knowledge
收稿日期: 2009-11-15 出版日期: 2010-09-30
CLC:  TP278  
基金资助: Project supported by the Important National Science and Technology Specific  Projects  (No.  2009ZX04014-031),  the  Science  and  Tech-
nology Pillar Program of Zhejiang Province (No. 2009C31120), and the  Zhejiang  Provincial  Natural  Science  Foundation  of  China  (No.
Z1080339)
通讯作者: Jing CHEN     E-mail: jingchen.zju@gmail.com
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引用本文:

Jing Chen, Shu-you Zhang, Zhan Gao, Li-xin Yang. Feature-based initial population generation for the optimization of job shop problems. Front. Inform. Technol. Electron. Eng., 2010, 11(10): 767-777.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910707    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I10/767

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