Pruning Redundancy in Answer Set Optimization Applied to Preventive Maintenance ...
summary
Defines miscoverage as the optimization objective for preventive maintenance, gives six concrete pruning constraints that identify prunable schedules (lagging, overserving, underserving, etc.) and proves these can be removed without losing any optimal solution. Implements the rules in multiple ASP encodings, runs clasp and wasp on 106 random instances (machines up to 16 components, timelines up to 64 steps), shows up to tenfold speedups and that combining pruning with unsatisfiable-core optimization solves all benchmarks while branch-and-bound benefits too.
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chapters (10)
- 0:00Pruning redundancy in ASO
- 1:24Preventive maintenance scheduling
- 5:43Miscoverage objective formalized
- 11:43Pruning symmetries in search
- 20:46Pruning constraints impact
- 24:03Experimental results
- 26:32Conclusions and future
- 28:00Q&A: Symmetries and weighting
- 29:03Q&A: Benchmark sets
- 30:50Q&A: Cactus plots and optimality
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