OPIS: an opportunistic factory scheduling system
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
Issues in the design of AI-based schedulers: A workshop report
AI Magazine - Reports from three of the 1990 Spring symposia and eight workshops held over the past two years
Scheduling of production processes
Scheduling of production processes
Meta-scheduling using dynamic scheduling knowledge
Scheduling of production processes
Intelligent scheduling
Knowledge-Based Interactive Scheduling of Multiproduct Batch Plants
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
Knowledge base reuse through constraint relaxation
Proceedings of the 3rd international conference on Knowledge capture
A study in applying case-based reasoning to engineering design: Mechanical bearing design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A knowledge-based scheduling system for Emergency Departments
Knowledge-Based Systems
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A generic framework facilitates the construction of knowledge-based scheduling systems. The authors have used it to implement scheduling systems for dye production, pipeline-fittings production, and heart surgery.Production management is crucial for achieving the timely and cost-effective execution of industrial production processes. In recent years, interest has increased in the use of artificial intelligence technologies for production planning and scheduling. However, scheduling research typically has been theoretical, has had a narrow focus, and has not covered adaptation to unforeseen events (see the "Scheduling problem" and "Previous scheduling research" sidebars).Our objective has been to use computer-based scheduling systems to enhance the problem-solving capabilities of human domain experts. During our research, we have developed a generic framework for building practical scheduling systems. This framework fosters the reuse of algorithms and the integration of knowledge-based technology into the organizational environment. It also supports dynamic adaptation. We successfully applied our framework in the implementation of three scheduling systems-that is, they all share the same system architecture and use similar problem-solving methodologies. The first two systems deal with serious real-life problems in the manufacturing industry: the rarely investigated continuous-flow scheduling problem and the widely known job-shop problem. The third system shows how concepts and techniques developed for industry can be transferred successfully to a medical domain.