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Large Neighborhood Beam Search for Domain-Independent Dynamic Programming

Authors Ryo Kuroiwa , J. Christopher Beck



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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

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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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