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Hybrid Optimization pp 11–62Cite as

Hybrid Modeling

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Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 45))

Abstract

The modeling practices of constraint programming (CP), artificial intelligence, and operations research must be reconciled and integrated if the computational benefits of combining their solution methods are to be realized in practice. This chapter focuses on CP and mixed integer/linear programming (MILP), in which modeling systems are most highly developed. It presents practical guidelines and supporting theory for the two types of modeling. It then suggests how an integrated modeling framework can be designed that retains, and even enhances, the modeling power of CP while allowing the full computational resources of both fields to be applied and combined. A series of examples are used to compare modeling practices in CP, MILP, and an integrated framework.

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Author information

Authors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, 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. , Department of Computer Science, Brown University, Providence, 02912, Rhode Island, USA

    Pascal van Hentenryck

  2. , Department of Electronics, Computer Scie, Università di Bologna, Viale Risorgimento 2, Bologna, 40136, Italy

    Michela Milano

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Hooker, J.N. (2011). Hybrid Modeling. In: van Hentenryck, P., Milano, M. (eds) Hybrid Optimization. Springer Optimization and Its Applications, vol 45. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1644-0_2

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