Abstract
This paper considers a constraint-based scheduling approach to the flexible jobshop, a generalization of the traditional jobshop scheduling where activities have a choice of machines. It studies both large neighborhood (LNS) and adaptive randomized decomposition (ARD) schemes, using random, temporal, and machine decompositions. Empirical results on standard benchmarks show that, within 5 minutes, both LNS and ARD produce many new best solutions and are about 0.5 % in average from the best-known solutions. Moreover, over longer runtimes, they improve 60 % of the best-known solutions and match the remaining ones. The empirical results also show the importance of hybrid decompositions in LNS and ARD.
Original language | English |
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Journal | Proceedings of the International Joint Conference on Artificial Intelligence |
Number of pages | 6 |
ISSN | 1045-0823 |
Publication status | Published - 2011 |
Externally published | Yes |
Event | 22nd International Joint Conference on Artificial Intelligence - Barcelona, Spain Duration: 16 Jul 2011 → 22 Jul 2011 Conference number: 22 |
Conference
Conference | 22nd International Joint Conference on Artificial Intelligence |
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Number | 22 |
Country/Territory | Spain |
City | Barcelona |
Period | 16/07/2011 → 22/07/2011 |