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. The authors' objective has been to use computer-based scheduling systems to enhance the problem-solving capabilities of human domain experts. They 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. The authors have used it to implement scheduling systems for dye production, pipeline-fittings production and heart surgery.
['Jürgen Sauer', 'Ralf Bruns']
Knowledge-based scheduling systems in industry and medicine