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An Investigation of Generic Approaches to Large Neighbourhood Search (Short Paper)

Authors Filipe Souza , Diarmuid Grimes , Barry O'Sullivan

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Author Details

Filipe Souza
  • Insight SFI Research Centre for Data Analytics, National University of Ireland Galway, Ireland
  • SFI Centre for Research Training in Artificial Intelligence, Cork, Ireland
  • School of Computer Science & IT, University College Cork, Ireland
Diarmuid Grimes
  • Munster Technological University, Cork, Ireland
  • SFI Centre for Research Training in Artificial Intelligence, Cork, Ireland
Barry O'Sullivan
  • Insight SFI Research Centre for Data Analytics, National University of Ireland Galway, Ireland
  • SFI Centre for Research Training in Artificial Intelligence, Cork, Ireland
  • School of Computer Science & IT, University College Cork, Ireland

Funding

Supported by SFI Centre for Research Training in Artificial Intelligence under Grant No. 18/CRT/6223 and SFI under Grant No. 12/RC/2289-P2, co-funded under the European Regional Development Fund.

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Filipe Souza, Diarmuid Grimes, and Barry O'Sullivan. An Investigation of Generic Approaches to Large Neighbourhood Search (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 39:1-39:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.39

Abstract

A bottleneck in the more wide-spread use of approaches such as Large Neighborhood Search is the need for domain-specific knowledge. To this end, a number of generic LNS methods have previously been proposed that automate the selection of variables in the neighborhood with the aim of reducing the expertise requirement. Recently a new generic approach, Improved Variable-Relationship Guided LNS (iVRG), was proposed that showed promising initial results. This method combines static information regarding problem structure and dynamic information from search performance in its neighborhood selection. In this work, we first show the generalisability of the approach by comparing it on two widely studied problems, car sequencing and steel mill slab, where it outperformed existing generic approaches. We then provide a detailed examination of iVRG, investigating its key components (static/dynamic information, the use of a Tournament Selection operator) to assess their individual impact and provide insight into iVRGs overall behavior.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Heuristic function construction
Keywords
  • Combinatorial Optimization
  • Metaheuristics
  • Large Neighborhood Search (LNS)
  • Machine Reassignment Problem
  • Car Sequencing Problem
  • Steel Mill Slab Problem

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References

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