Maintenance scheduling problems as benchmarks for constraint algorithms

The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the electric power industry: optimally scheduling preventive maintenance of power generating units within a power plant. We sho...

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Veröffentlicht in:Annals of mathematics and artificial intelligence Jg. 26; H. 1-4; S. 149 - 170
Hauptverfasser: Frost, Daniel, Dechter, Rina
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Dordrecht Springer Nature B.V 01.02.1999
Schlagworte:
Algorithms
Machine learning
Maintenance management
Optimization
Power plants
Preventive maintenance
Schedules
Scheduling
Search algorithms
Task scheduling
ISSN:1012-2443, 1573-7470
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Zusammenfassung:The paper focuses on evaluating constraint satisfaction search algorithms on application based random problem instances. The application we use is a well-studied problem in the electric power industry: optimally scheduling preventive maintenance of power generating units within a power plant. We show how these scheduling problems can be cast as constraint satisfaction problems and used to define the structure of randomly generated non-binary CSPs. The random problem instances are then used to evaluate several previously studied algorithms. The paper also demonstrates how constraint satisfaction can be used for optimization tasks. To find an optimal maintenance schedule, a series of CSPs are solved with successively tighter cost-bound constraints. We introduce and experiment with an “iterative learning” algorithm which records additional constraints uncovered during search. The constraints recorded during the solution of one instance with a certain cost-bound are used again on subsequent instances having tighter cost-bounds. Our results show that on a class of randomly generated maintenance scheduling problems, iterative learning reduces the time required to find a good schedule.
Bibliographie:ObjectType-Article-1
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ISSN:1012-2443
1573-7470
DOI:10.1023/A:1018906911996