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Parallel Composition of Scheduling Solvers

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Integration of AI and OR Techniques in Constraint Programming (CPAIOR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9676))

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Abstract

Recent work in model combinators, as well as projects like G12 and SIMPL, achieved significant progress in automating the generation of complex and hybrid solvers from high-level model specifications. This paper extends model combinators into the scheduling domain. This is of particular interest as, today, both Constraint Programming (CP) and Mixed-Integer Programming (MIP) perform well on scheduling problems providing different capabilities and trade-offs. The ability to construct hybrid scheduling solvers to leverage the strengths of both technologies as well as multiple problem encodings through high-level model combinators provides new opportunities. Complex parallel hybrids can be synthesized with minimal effort on the part of the user and provide substantial performance benefits over standalone solvers.

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Notes

  1. 1.

    Line 3 refers to the search procedure defined earlier with a closure and named search.

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Authors and Affiliations

  1. University of Connecticut, Storrs, CT, 06269-2155, USA

    Daniel Fontaine & Laurent Michel

  2. University of Michigan, Ann Arbor, USA

    Pascal Van Hentenryck

Authors
  1. Daniel Fontaine
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  2. Laurent Michel
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  3. Pascal Van Hentenryck
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Correspondence to Laurent Michel .

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Editors and Affiliations

  1. Université Laval, Quebec, Québec, Canada

    Claude-Guy Quimper

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Fontaine, D., Michel, L., Van Hentenryck, P. (2016). Parallel Composition of Scheduling Solvers. In: Quimper, CG. (eds) Integration of AI and OR Techniques in Constraint Programming. CPAIOR 2016. Lecture Notes in Computer Science(), vol 9676. Springer, Cham. https://doi.org/10.1007/978-3-319-33954-2_12

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  • DOI: https://doi.org/10.1007/978-3-319-33954-2_12

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  • Online ISBN: 978-3-319-33954-2

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