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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))

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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