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Large Neighborhood Search

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Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 272))

Abstract

In the last 15 years, heuristics based on large neighborhood search (LNS) and the variant adaptive large neighborhood search (ALNS) have become some of the most successful paradigms for solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood through the use of heuristics. Using large neighborhoods makes it possible to find better candidate solutions in each iteration and hence follow a more promising search path. Starting from the general framework of large neighborhood search, we study in depth adaptive large neighborhood search, discussing design ideas and properties of the framework. Application of large neighborhood search methods in routing and scheduling are discussed. We end the chapter by presenting the related framework of very large-scale neighborhood search (VLSN) and discuss parallels to LNS, before drawing some conclusions about algorithms exploiting large neighborhoods.

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  1. DTU Management Engineering, Technical University of Denmark, Lyngby, Denmark

    David Pisinger & Stefan Ropke

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  1. Department of Mathematics and Industrial Engineering, Polytechnique Montréal, Montreal, QC, Canada

    Michel Gendreau

  2. Département d’informatique et de recherche opérationnelle, Université de Montréal, Montreal, QC, Canada

    Jean-Yves Potvin

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Pisinger, D., Ropke, S. (2019). Large Neighborhood Search. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 272. Springer, Cham. https://doi.org/10.1007/978-3-319-91086-4_4

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