Name
Abstract | ||
|---|---|---|
This paper presents an optimal constraint programming approach for the open-shop scheduling problem, which integrates recent constraint propagation and branching techniques with new upper bound heuristics. Randomized restart policies combined with nogood recording allow us to search diversification and learning from restarts. This approach is compared with the best-known metaheuristics and exact algorithms, and it shows better results on a wide range of benchmark instances. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1287/ijoc.1100.0446 | Informs Journal on Computing |
Keywords | DocType | Volume |
benchmark instance,open-shop problem,optimal constraint programming approach,better result,wide range,open-shop scheduling problem,nogood recording,exact algorithm,new upper bound heuristics,best-known metaheuristics,recent constraint propagation,artificial intelligence,constraint programming | Journal | 24 |
Issue | ISSN | Citations |
2 | 1091-9856 | 5 |
PageRank | References | Authors |
0.45 | 21 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Arnaud Malapert | 1 | 31 | 4.50 |
| Hadrien Cambazard | 2 | 161 | 19.07 |
| Christelle Gué/ret | 3 | 5 | 0.45 |
| Narendra Jussien | 4 | 444 | 34.84 |
| André/ Langevin | 5 | 5 | 0.45 |
| Louis-Martin Rousseau | 6 | 888 | 63.71 |