46 lines
4.6 KiB
TeX
46 lines
4.6 KiB
TeX
\noindent{}In previous chapters we explored the compilation of constraint models for a \gls{solver} as a definitive linear process, but to solve real-world problems \gls{meta-search} algorithms a.re often used.
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These methods usually require solving almost the same combinatorial problem repeatedly, with only slight modifications, thousands of times.
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Examples of these methods are:
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\begin{itemize}
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\item Multi-objective search \autocite{jones-2002-multi-objective}.
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Optimising multiple objectives is often not supported directly in solvers.
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Instead, it can be solved using a \gls{meta-search} approach: find a solution to a (single-objective) problem, then add more constraints to the original problem and repeat.
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\item \gls{lns} \autocite{shaw-1998-local-search}.
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This is a very successful \gls{meta-search} algorithm to quickly improve solution quality.
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After finding a (sub-optimal) solution to a problem, constraints are added to restrict the search in the \gls{neighbourhood} of that solution.
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When a new solution is found, the constraints are removed, and constraints for a new \gls{neighbourhood} are added.
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\item Online Optimisation \autocite{jaillet-2021-online}.
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These techniques can be employed when the problem rapidly changes.
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A problem instance is continuously updated with new data, such as newly available jobs to be scheduled or customer requests to be processed.
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\item Diverse Solution Search \autocite{hebrard-2005-diverse}.
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Here we aim to provide a set of solutions that are sufficiently different from each other in order to give human decision makers an overview of the solution space.
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Diversity can be achieved by repeatedly solving a problem instance with different objectives.
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\item Interactive Optimisation \autocite{belin-2014-interactive}.
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In some scenarios it might be useful to allow a user to directly provide feedback on solutions found by the solver.
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The feedback in the form of constraint are added back into the problem, and a new solution is generated.
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Users may also take back some earlier feedback and explore different aspects of the problem to arrive at the best solution that suits their needs.
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\end{itemize}
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All of these examples have in common that a problem instance is solved, new constraints are added, the resulting instance is solved again, and constraints may be removed again.
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The usage of these methods is not new to constraint modelling, and they have proven to be very useful \autocite{schrijvers-2013-combinators, rendl-2015-minisearch, schiendorfer-2018-minibrass, ek-2020-online, ingmar-2020-diverse}.
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In its most basic form, a simple scripting language is sufficient to implement these methods, by repeatedly calling on the constraint modelling infrastructure to compile and solve the adjusted constraint models.
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While improvements of the compilation of constraint models, such as the ones discussed in previous chapters, can increase the performance of these approaches, the overhead of re-compiling an almost identical model may still prove prohibitive, warranting direct support from the constraint modelling infrastructure.
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In this chapter we introduce two methods to provide this support:
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\begin{itemize}
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\item We introduce the notion of restart-based \gls{meta-search} algorithms.
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Using a minimal extension to a \cml\ and its target solver, we can model some \gls{meta-search} algorithms and compile \gls{meta-search} algorithms into efficient solver-level specifications based on solver restarts, avoiding re-compilation all-together.
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\item Alternatively, we can add an incremental interface for adding and removing constraints to the infrastructure of the \cml{}.
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Although this does not avoid the need for re-compilation, it can reduce the work to only the part of the constraint model that has changed.
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This approach can be used when an algorithm cannot be described using restart-based \gls{meta-search} or required extension is not available for the solver.
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\end{itemize}
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The rest of the chapter is organised as follows.
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\Cref{sec:6-modelling} discusses the declarative modelling of restart-based \gls{meta-search} algorithms that can be modelled directly in a \cml{}.
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\Cref{sec:6-solver-extension} introduces the method to compile these \gls{meta-search} specifications into efficient solver-level specifications that only require a small extension of existing \glspl{solver}.
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\Cref{sec:6-incremental-compilation} introduces the alternative method that extends the constraint modelling infrastructure with an interface to add and remove constraints from an existing model while avoiding recompilation.
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\Cref{sec:inc-experiments} reports on the experimental results of both approaches.
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