Skip to Main Content
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.