| Associating Memory Through Case-Based Immune Mechanisms for Dynamic Job-Shop Scheduling |
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| 作者姓名： | 尹文君 刘民 吴澄 |
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| 作者单位： | DepartmentofAutomation,TsinghuaUniversity,Beijing100084,China |
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| 基金项目： | the National Natural Science Foundation of China (No. 60004010) and the National High-Tech Research and Development (863) Program of China (No. 2001AA411020) |
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| 摘 要： | Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms. A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms. A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods. In static environments, the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually. The results with dynamic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse.
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| 关 键 词： | 免疫算法 2度相似性 机器学习 联想记忆 动态工作-功图表 案例基推理 |
Associating Memory Through Case-Based Immune Mechanisms for Dynamic Job-Shop Scheduling |
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| YIN Wenjun,LIU Min,WU Cheng.Associating Memory Through Case-Based Immune Mechanisms for Dynamic Job-Shop Scheduling[J].Tsinghua Science and Technology,2004,9(4):422-427. |
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| Authors: | YIN Wenjun LIU Min WU Cheng |
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| Institution: | YIN Wenjun,LIU Min,WU Cheng Department of Automation,Tsinghua University,Beijing 100084,China |
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| Abstract: | Knowledge plays an active role in job-shop scheduling, especially in dynamic environments. A novel case-based immune framework was developed for static and dynamic job-shop problems, using the associative memory and knowledge reuse from case-based reasoning (CBR) and immune response mechanisms. A 2-level similarity index which combines both job routing and problem solution characteristics based on DNA matching ideas was defined for both the CBR and immune algorithms. A CBR-embedded immune algorithms (CBR-IAs) framework was then developed focusing on case retrieval and adaptation methods. In static environments, the CBR-IAs have excellent population diversity and fast convergence which are necessary for dynamic problems with jobs arriving and leaving continually. The results with dy-namic scheduling problems further confirm the CBR-IAs effectiveness as a problem solving method with knowledge reuse. |
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| Keywords: | case-based reasoning immune algorithm 2-level similarity machine learning dynamic job-shop scheduling |
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