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International Conference on Principles and Practice of Constraint Programming

CP 2019: Principles and Practice of Constraint Programming pp 268–283Cite as

Improved Job Sequencing Bounds from Decision Diagrams

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11802))

Abstract

We introduce a general method for relaxing decision diagrams that allows one to bound job sequencing problems by solving a Lagrangian dual problem on a relaxed diagram. We also provide guidelines for identifying problems for which this approach can result in useful bounds. These same guidelines can be applied to bounding deterministic dynamic programming problems in general, since decision diagrams rely on DP formulations. Computational tests show that Lagrangian relaxation on a decision diagram can yield very tight bounds for certain classes of hard job sequencing problems. For example, it proves for the first time that the best known solutions for Biskup-Feldman instances are within a small fraction of 1% of the optimal value, and sometimes optimal.

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Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, USA

    John N. Hooker

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  1. John N. Hooker
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Correspondence to John N. Hooker .

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Editors and Affiliations

  1. INRA, Castanet Tolosan, France

    Thomas Schiex

  2. INRA, Castanet Tolosan, France

    Simon de Givry

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Hooker, J.N. (2019). Improved Job Sequencing Bounds from Decision Diagrams. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_16

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  • DOI: https://doi.org/10.1007/978-3-030-30048-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30047-0

  • Online ISBN: 978-3-030-30048-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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