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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| Vydané v: | Annals of mathematics and artificial intelligence Ročník 26; číslo 1-4; s. 149 - 170 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Dordrecht
Springer Nature B.V
01.02.1999
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| ISSN: | 1012-2443, 1573-7470 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Dechter, Rina Frost, Daniel |
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| CitedBy_id | crossref_primary_10_1016_j_artint_2013_01_002 crossref_primary_10_1002_tee_70050 crossref_primary_10_1016_j_amc_2007_08_064 crossref_primary_10_1016_j_ins_2007_03_030 crossref_primary_10_1287_opre_1060_0301 |
| Cites_doi | 10.1145/361219.361224 10.1111/j.1467-8640.1993.tb00310.x 10.1016/0004-3702(90)90046-3 10.1016/0004-3702(85)90041-4 10.1109/59.141807 10.1109/T-PAS.1975.31894 10.1109/T-PAS.1975.31996 10.1109/TSMC.1976.4309548 10.1109/59.141779 10.1016/0004-3702(80)90051-X |
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| References | T.M. Al-Khamis (325538_CR1) 1992; 7 P. Prosser (325538_CR15) 1993; 9 325538_CR17 J.F. Dopazo (325538_CR6) 1975; 94 H. Kin (325538_CR13) 1996; 12 D. Frost (325538_CR8) 1997 325538_CR10 R.M. Haralick (325538_CR12) 1980; 14 J.R. Bitner (325538_CR3) 1975; 18 325538_CR11 A.K. Mackworth (325538_CR14) 1985; 25 J. Yellen (325538_CR16) 1992; 7 325538_CR2 R. Dechter (325538_CR5) 1992 R. Dechter (325538_CR4) 1990; 41 325538_CR7 325538_CR9 |
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| StartPage | 149 |
| SubjectTerms | Algorithms Machine learning Maintenance management Optimization Power plants Preventive maintenance Schedules Scheduling Search algorithms Task scheduling |
| Title | Maintenance scheduling problems as benchmarks for constraint algorithms |
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