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Large Neighborhood Beam Search for Domain-Independent Dynamic Programming

Authors Ryo Kuroiwa , J. Christopher Beck

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Author Details

Ryo Kuroiwa
  • Department of Mechanical and Industrial Engineering, University of Toronto, Canada
J. Christopher Beck
  • Department of Mechanical and Industrial Engineering, University of Toronto, Canada

Funding

This research is supported by the Natural Sciences and Engineering Research Council of Canada.

Cite AsGet BibTex

Ryo Kuroiwa and J. Christopher Beck. Large Neighborhood Beam Search for Domain-Independent Dynamic Programming. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 23:1-23:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.23

Abstract

Large neighborhood search (LNS) is an algorithmic framework that removes a part of a solution and performs search in the induced search space to find a better solution. While LNS shows strong performance in constraint programming, little work has combined LNS with state space search. We propose large neighborhood beam search (LNBS), a combination of LNS and state space search. Given a solution path, LNBS removes a partial path between two states and then performs beam search to find a better partial path. We apply LNBS to domain-independent dynamic programming (DIDP), a recently proposed generic framework for combinatorial optimization based on dynamic programming. We empirically show that LNBS finds better quality solutions than a state-of-the-art DIDP solver in five out of nine benchmark problem types with a total of 8570 problem instances. In particular, LNBS shows a significant improvement over the existing state-of-the-art DIDP solver in routing and scheduling problems.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Discrete space search
Keywords
  • Large Neighborhood Search
  • Dynamic Programming
  • State Space Search
  • Combinatorial Optimization

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Supplementary Materials

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