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Jip J. Dekker 2021-07-29 15:47:23 +10:00
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Real world problems, such as scheduling and routing problems, are often solved by creating constraint models.
These models are automatically rewritten for the use of dedicated solver programs.
Over the years, both the size of the problems and approaches taken by the solver programs have significantly changed.
This has meant that for some applications the rewriting process has become prohibitively inefficient.
This thesis proposes a more efficient structure for the rewriting process that makes constraint modelling a more powerful and attractive approach.
Real world problems, such as scheduling and routing problems, are often solved by creating high-level models.
These high-level models are automatically rewritten to input suitable for dedicated solver programs.
This thesis proposes an architecture for the rewriting process that makes it significantly more efficient and its results easier to solve.
It also supports solving more challenging real world problems that require solving a sequence of related high-level models.
This defines a new state of the art for model rewriting.