35 lines
3.0 KiB
TeX
35 lines
3.0 KiB
TeX
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\chapter{Abstract}\label{ch:abstract}
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\vspace{-8em}
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\noindent{}\Cmls{} are a prominent way to model and solve real world problems.
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They are used in areas such as scheduling, supply chain management, and transportation, among many others.
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The \gls{rewriting} process of a \cml{} transforms a \cmodel{} into a \gls{slv-mod}, the input required by the program that solves the problem.
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In the past, these languages served mainly as a standardized interface between different \solvers{} and the \gls{rewriting} required was negligible.
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However, \cmls{} have evolved to include functionality that is not directly supported by the target \solvers{}.
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As such, the \gls{rewriting} process has become more important and complex.
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\minizinc{}, one such language, was originally designed for constraint programming \solvers{}, whose \glspl{slv-mod} contain few, highly complex \constraints{}.
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The same \minizinc{} models can now target mixed integer programming and Boolean satisfiability \solvers{}, resulting in numerous, very simple \constraints{}.
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Distinctively, \minizinc{}'s \gls{rewriting} process is founded on its functional language.
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It generates \glspl{slv-mod} through the application of increasingly complex \minizinc{} functions from \solver{}-specific libraries.
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Consequently, the performance of the functional evaluation of the language can be a limiting factor.
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For many applications, the current \minizinc{} implementation now requires a significant, and sometimes prohibitive, amount of time to rewrite \instances{}.
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This problem is exacerbated by the emerging use of \gls{meta-optimization} algorithms, which require the rewriting and solving of a sequence of closely related \instances{}.
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In this thesis we revisit the \gls{rewriting} of functional \cmls{} into \glspl{slv-mod}.
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We design and evaluate an architecture for \cmls{} that can accommodate its modern uses.
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At its core lies a formal execution model that allows us to rewrite \cmodels{} efficiently and actively manage the \gls{slv-mod}.
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We show how it can better detect and eliminate parts of the model that have become unused.
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The architecture is extended using a range of well-known simplification techniques to ensure the quality of the produced \glspl{slv-mod}.
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In addition, we incorporate new analysis techniques to avoid the use of \glspl{reif} or replace them with \glspl{half-reif}, where possible.
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The architecture is designed to incorporate incremental \constraint{} modelling in two ways.
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Primarily, the \gls{rewriting} process is fully incremental: changes made to the \instance{} through a provided interface require minimal addition \gls{rewriting} effort.
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Moreover, we introduce \gls{rbmo}, a way to specify \gls{meta-optimization} algorithms directly in \minizinc{}.
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These specifications are executed by a normal \minizinc{} \solver{}, requiring only a slight extension of its capabilities.
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Together, the functionality of this architecture helps to make \cmls{} a more powerful and attractive approach to solve real world problems.
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