Large Neighborhood Search and Adaptive Randomized Decompositions for Flexible Jobshop Scheduling

Dario Pacino, Pascal Van Hentenryck

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    Abstrakt

    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 de- composition (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 so- lutions and match the remaining ones. The empir- ical results also show the importance of hybrid de- compositions in LNS and ARD.
    OriginalsprogEngelsk
    TidsskriftIJCAI Proceedings - International Joint Conference on Artificial Intelligence
    Antal sider6
    ISSN1045-0823
    StatusUdgivet - 2011

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