Solver-Independent Large Neighbourhood Search

Dekker, Jip J.; Banda, Maria García de la; Schutt, Andreas; Stuckey, Peter J.; Tack, Guido

doi:10.1007/978-3-319-98334-9_6

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The use of LNS in MIP (Danna, Rothberg, and Pape 2005;Rothberg 2007;Ghosh 2007) and CP (Perron, Shaw, and Furnon 2004;Berthold et al 2011;Björdal et al 2020) is well explored. For declarative LNS neighbourhood definitions, the constraint modelling languages were extended to support solver-independent LNS search (Dekker et al 2018;Björdal et al 2018;Rendl et al 2015). Our approach merely requires dedicated predicates that can be defined by rules, and it offers unlimited power for neighbourhood definition by external plugins.…”

Section: Related Workmentioning

confidence: 99%

Large-Neighbourhood Search for Optimisation in Answer-Set Solving

Eiter

Geibinger

Ruiz

et al. 2022

AAAI

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While Answer-Set Programming (ASP) is a prominent approach to declarative problem solving, optimisation problems can still be a challenge for it. Large-Neighbourhood Search (LNS) is a metaheuristic for optimisation where parts of a solution are alternately destroyed and reconstructed that has high but untapped potential for ASP solving. We present a framework for LNS optimisation in answer-set solving, in which neighbourhoods can be specified either declaratively as part of the ASP encoding, or automatically generated by code. To effectively explore different neighbourhoods, we focus on multi-shot solving as it allows to avoid program regrounding. We illustrate the framework on different optimisation problems, some of which are notoriously difficult, including shift planning and a parallel machine scheduling problem from semi-conductor production which demonstrate the effectiveness of the LNS approach.

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Section: Related Workmentioning

confidence: 99%

Large-Neighbourhood Search for Optimisation in Answer-Set Solving

Eiter

Geibinger

Ruiz

et al. 2022

AAAI

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“…For problem-specific cases, where these annotations are not sufficient, we extend MiniZinc to give modellers access to the current solution via a generic function sol(x), which returns, for each variable and parameter x, the value of x in the current solution. This is analogous to the use of the function sol() in MiniSearch (Rendl et al 2015) and other extensions of MiniZinc (Dekker et al 2018) to refer to the previous solution to a problem. In addition, we give modellers access to function has_sol(x) to test whether x actually exists in the current solution, and to the now parameter for use in their time constraints.…”

Section: Modelling Online Problemsmentioning

confidence: 99%

Modelling and Solving Online Optimisation Problems

Banda²,

Schutt

et al. 2020

AAAI

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Many optimisation problems are of an online—also called dynamic—nature, where new information is expected to arrive and the problem must be resolved in an ongoing fashion to (a) improve or revise previous decisions and (b) take new ones. Typically, building an online decision-making system requires substantial ad-hoc coding to ensure the offline version of the optimisation problem is continually adjusted and resolved. This paper defines a general framework for automatically solving online optimisation problems. This is achieved by extending a model of the offline optimisation problem, from which an online version is automatically constructed, thus requiring no further modelling effort. In doing so, it formalises many of the aspects that arise in online optimisation problems. The same framework can be applied for automatically creating sliding-window solving approaches for problems that have a large time horizon. Experiments show we can automatically create efficient online and sliding-window solutions to optimisation problems.

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