Abstract
Scheduling of operations in a production process is usually seen as a combinatorial search for a feasible and perhaps optimal plan. In actual industrial practice however, constraints and optimization criteria can often not be given exactly because they are unknown or vague. Therefore a combinatorial approach often does not meet the actual requirements. Furthermore, the representation of all potential alternatives of the production process would lead to a combinatorial explosion that cannot be solved in a reasonable time frame.
In this paper case-based reasoning is applied to reactive scheduling to model the experience of skilled human operators in reacting to unforeseen events in the production process. Old solutions stored in a case base are used to solve new problems. Case-based reasoning is supported by fuzzy reasoning on a constraint-based representation to find and adapt old cases and to evaluate them. An iterative improvement method is further used to optimize solutions found by case-based reasoning. The approach is illustrated by means of an application in the steel industry.
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© 1995 IFIP International Federation for Information Processing
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Dorn, J. (1995). Case-based reactive scheduling. In: Kerr, R., Szelke, E. (eds) Artificial Intelligence in Reactive Scheduling. IFIP Advances in Information and Communication Technology. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-34928-2_4
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DOI: https://doi.org/10.1007/978-0-387-34928-2_4
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-5041-2889-6
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