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Hybrid Modeling, John N. Hooker
Hybrid Modeling, John N. Hooker
John Hooker
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 …
A Declarative Modeling Framework That Integrates Solution Methods, John N. Hooker, Hak-Jin Kim, G. Ottosson
A Declarative Modeling Framework That Integrates Solution Methods, John N. Hooker, Hak-Jin Kim, G. Ottosson
John Hooker
Constraint programming offers modeling features and solution methods that are unavailable in mathematical programming but are often flexible and efficient for scheduling and other combinatorial problems. Yet mathematical programming is well suited to declarative modeling languages and is more efficient for some important problem classes. This raises this issue as to whether the two approaches can be combined in a declarative modeling framework. This paper proposes a general declarative modeling system in which the conditional structure of the constraints shows how to integrate any "checker" and any special-purpose "solver". In particular this integrates constraint programming and optimization methods, because the …