Skip to main content
SpringerLink
Log in

Large Neighborhood Search

  • Chapter
  • First Online:
Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 146))

Abstract

Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better candidate solutions in each iteration and hence traverse a more promising search path. Starting from the large neighborhood search method, we give an overview of very large scale neighborhood search methods and discuss recent variants and extensions like variable depth search and adaptive large neighborhood search.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Ahuja, R.K., Ergun, Ö., Orlin, J.B., Punnen, A.P.: A survey of very large-scale neighborhood search techniques. Discrete Appl. Math. 123, 75–102 (2002)

    Article  Google Scholar 

  2. Ahuja, R.K., Orlin, J.B., Sharma, D.: New neighborhood search structures for the capacitated minimum spanning tree problem. Technical Report 99–2, 1999

    Google Scholar 

  3. Applegate, D.L., Bixby, R.E., Chvátal, V., Cook, W.J.: The Traveling Salesman Problem: A Computational Study. Princeton University Press, Princeton, NJ (2006)

    Google Scholar 

  4. Bent, R., Van Hentenryck, P.: A two-stage hybrid local search for the vehicle routing problem with time windows. Transport. Sci. 38(4), 515–530 (2004)

    Article  Google Scholar 

  5. Bent, R., Van Hentenryck, P.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problem with time windows. Comput. Oper. Res. 33(4), 875–893 (2006)

    Article  Google Scholar 

  6. Brueggemann, T., Hurink, J.L.: Matching based exponential neighborhoods for parallel machine scheduling. Technical Report Memorandum No. 1773, (2005)

    Google Scholar 

  7. Brueggemann, T., Hurink, J.L.: Two exponential neighborhoods for single machine scheduling. Technical Report Memorandum No. 1776, 2005

    Google Scholar 

  8. Brueggemann, T., Hurink, J.: Two very large-scale neighborhoods for single machine scheduling. OR Spectr. 29, 513–533 (2007)

    Article  Google Scholar 

  9. Carchrae, T., Beck, J.C.: Cost-based large neighborhood search. In Workshop on the Combination of Metaheuristic and Local Search with Constraint Programming Techniques, 2005

    Google Scholar 

  10. Caseau, Y., Laburthe, F., Silverstein, G.: A meta-heuristic factory for vehicle routing problems. Lect. Notes Comput. Sci. 1713, 144–159 (1999)

    Article  Google Scholar 

  11. Cordeau, J.-F., Laporte, G., Pasin, F., Ropke, S.: Scheduling technicians and tasks in a telecommunications company. J. Scheduling (2010) Forthcoming

    Google Scholar 

  12. Cornuejols, G., Naddef, D., Pulleyblank, W.R.: Halin graphs and the traveling salesman problem. Math. Program. 26, 287–294 (1983)

    Article  Google Scholar 

  13. De Franceschi, R., Fischetti, M., Toth, P.: A new ILP-based refinement heuristic for vehicle routing problems. Math. Program. 105, 471–499 (2006)

    Article  Google Scholar 

  14. Dowsland, K.A.: Nurse scheduling with tabu search and strategic oscillation. Eur. J. Oper. Res. 106, 393–407 (1998)

    Article  Google Scholar 

  15. Dumitrescu, I., Ropke, S., Cordeau, J.-F., Laporte, G.: The traveling salesman problem with pickup and delivery: polyhedral results and a branch-and-cut algorithm. Math. Program. 121, 269–305 (2009)

    Article  Google Scholar 

  16. Flood, M.M.: The traveling salesman problem. Oper. Res. 4(1), 61–75 (1956)

    Article  Google Scholar 

  17. Gamboa, D., Osterman, C., Rego, C., Glover, F.: An experimental evaluation of ejection chain algorithms for the traveling salesman problem. Technical report, School of Business Administration, University of Mississippi, 2006

    Google Scholar 

  18. Gendreau, M., Guertin, F., Potvin, J.-Y., Seguin, R.: Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Technical Report 98-10, 1998

    Google Scholar 

  19. Glover, F.: Ejection chains, reference structures, and alternating path algorithms for the traveling salesman problem. Technical Report, 1992

    Google Scholar 

  20. Glover, F., Rego, C.: Ejection chain and filter-and-fan methods in combinatorial optimization. 4OR: A Q. J. Oper. Res. 4, 263–296 (2006)

    Article  Google Scholar 

  21. Godard, D., Laborie, P., Nuijten, W.: Randomized large neighborhood search for cumulative scheduling. In: Proceedings of the 15th International Conference on Automated Planning and Scheduling (ICAPS 2005), pp. 81–89, Monterey, CA, USA, 5–10 June 2005

    Google Scholar 

  22. Goel, A.: Vehicle scheduling and routing with driver’s working hours. Transport. Sci. (2009) Forthcoming

    Google Scholar 

  23. Goel, A., Gruhn, V.: A general vehicle routing problem. Eur. J. Oper. Res. 191(3), 650–660 (2008)

    Article  Google Scholar 

  24. Gutin, G., Karapetyan, D.: Local search heuristics for the multidimensional assignment problem. In: Proceedings of the Golumbic Festschrift, vol. 5420, pp. 100–115 (2009)

    Google Scholar 

  25. Hansen, P., Mladenović, N.: Variable neighborhood search: Principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)

    Article  Google Scholar 

  26. Hurink, J.: An exponential neighborhood for a one machine batching problem. OR-Spektr. 21, 461–476 (1999)

    Article  Google Scholar 

  27. Kilby, P., Prosser, P., Shaw, P.: Guided local search for the vehicle routing problem. In: Proceedings of the 2nd International Conference on Metaheuristics, Sophia-Antipolis, France, July 1997

    Google Scholar 

  28. Laborie, P., Godard, D.: Self-adapting large neighborhood search: Application to single-mode scheduling problems. Technical Report TR-07-001, ILOG, 2007

    Google Scholar 

  29. Lin, S., Kernighan, B.: An effective heuristic algorithm for the traveling salesman problem. Oper. Res. 21, 498–516 (1973)

    Article  Google Scholar 

  30. Mester, D., Bräysy, O.: Active guided evolution strategies for large-scale vehicle routing problems with time windows. Comput. Oper. Res. 32, 1593–1614 (2005)

    Article  Google Scholar 

  31. Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)

    Article  Google Scholar 

  32. Muller, L.F.: An adaptive large neighborhood search algorithm for the resource-constrained project scheduling problem. In: Proceedings of MIC 2009: The VIII Metaheuristics International Conference, Hamburg, Germany

    Google Scholar 

  33. Nagata, Y., Bräysy, O.: A powerful route minimization heuristic for the vehicle routing problem with time windows. Oper. Res. Lett. 37, 333–338 (2009)

    Article  Google Scholar 

  34. Palpant, M., Artigues, C.C., Michelon, P.: LSSPER: Solving the resource-constrained project scheduling problem with large neighbourhood search. Ann. Oper. Res., 131, 237–257 (2004)

    Article  Google Scholar 

  35. Perron, L.: Fast restart policies and large neighborhood search. In: Proceedings of CP-AI-OR’2003, Montreal, Canada 2003

    Google Scholar 

  36. Perron, L., Shaw, P.: Parallel large neighborhood search. In: Proceedings of RenPar’15, La Colle sur Loup, France 2003

    Google Scholar 

  37. Petersen, H.L., Madsen, O.B.G.: The double travelling salesman problem with multiple stacks – formulation and heuristic solution approaches. Eur. J. Oper. Res. 198(1), 139–147 (2009)

    Article  Google Scholar 

  38. Phillips, J.M., Punnen, A.P., Kabadi, S.N.: A linear time algorithm for the bottleneck traveling salesman problem on a Halin graph. Inf. Process. Lett., 67, 105–110 (1998)

    Article  Google Scholar 

  39. Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8), 2403–2435 (2007)

    Article  Google Scholar 

  40. Prescott-Gagnon, E., Desaulniers, G., Rousseau, L.-M.: A branch-and-price-based large neighborhood search algorithm for the vehicle routing problem with time windows. Technical Report G-2007-67, GERAD, Montreal, QC, Canada, September 2007

    Google Scholar 

  41. Punnen, A.P.: The traveling salesman problem: New polynomial approximation algorithms and domination analysis. J. Inf. Optimization Sci., 22, 191–206 (2001)

    Google Scholar 

  42. Rego, C., Gamboa, D., Glover. F.: Data structures and ejection chains for solving large scale traveling salesman problems. Eur. J. Oper. Res. 160, 154–171 (2006)

    Google Scholar 

  43. Ropke, S.: Parallel large neighborhood search – a software framework. In: Proceedings of MIC 2009: The VIII Metaheuristics International Conference. Hamburg, Germany

    Google Scholar 

  44. Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transport. Sci., 40(4), 455–472 (2006)

    Article  Google Scholar 

  45. Ropke, S., Pisinger, D.: A unified heuristic for a large class of vehicle routing problems with backhauls. Eur. J. Oper. Res. 171, 750–775 (2006)

    Article  Google Scholar 

  46. Ross, P.: Hyper-heuristics. In: Burke, E.K., Kendall, G. (eds.) Introductory Tutorials in Optimisation, Decision Support and Search Methodology, Chapter 17, pp. 529–556. Springer, New York, NY (2005)

    Google Scholar 

  47. Rousseau, L.-M., Gendreau, M., Pesant, G.: Using constraint-based operators to solve the vehicle routing problem with time windows. J. Heuristics 8, 43–58 (2002)

    Article  Google Scholar 

  48. Sarvanov, V.I., Doroshko, N.N.: Approximate solution of the traveling salesman problem by a local algorithm with scanning neighborhoods of factorial cardinality in cubic time. In: Software: Algorithms and Programs, no. 31, pp. 11–13. Mathematical Institute of Belorussian Academy of Science, Minsk (1981) (in Russian)

    Google Scholar 

  49. Schrimpf, G., Schneider, J., Stamm-Wilbrandt, H., Dueck, G.: Record breaking optimization results using the ruin and recreate principle. J. Comput. Phys., 159(2), 139–171 (2000)

    Article  Google Scholar 

  50. Shaw, P.: Using constraint programming and local search methods to solve vehicle routing problems. In: CP-98 (Fourth International Conference on Principles and Practice of Constraint Programming). Lect. Notes Comput. Sci., 1520, 417–431 (1998)

    Google Scholar 

  51. Sontrop, H., van der Horn, P., Uetz, M.: Fast ejection chain algorithms for vehicle routing with time windows. Lect. Notes Comput. Sci. 3636, 78–89 (2005)

    Article  Google Scholar 

  52. Thompson, P.M.: Local search algorithms for vehicle routing and other combinatorial problems. PhD Thesis, Operations Research Center, MIT (1988)

    Google Scholar 

  53. Thompson, P.M., Psaraftis, H.N.: Cyclic transfer algorithms for multivehicle routing and scheduling problems. Oper. Res., 41 (1993)

    Google Scholar 

  54. Toth, P., Vigo, D.: An overview of vehicle routing problems. In: Toth, P., Vigo, D. (eds.) The Vehicle Routing Problem, vol. 9 of SIAM Monographs on Discrete Mathematics and Applications, Chapter 1, pp. 1–26. SIAM, Philadelphia, PA (2002)

    Google Scholar 

  55. Winter, P.: Steiner problem in Halin networks. Discrete Appl. Math., 17, 281–294 (1987)

    Article  Google Scholar 

  56. Yagiura, M., Ibaraki, T., Glover, F.: A path relinking approach with ejection chains for the generalized assignment problem. Eur. J. Oper. Res., 169, 548–569 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Management Engineering, Technical University of Denmark, Produktionstorvet, Building 426, 2800 Kgs., Lyngby, Denmark

    David Pisinger

  2. Department of Transport, Technical University of Denmark, Bygningstorvet, Building 115, 2800 Kgs., Lyngby, Denmark

    Stefan Ropke

Authors
  1. David Pisinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Ropke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David Pisinger .

Editor information

Editors and Affiliations

  1. , Département de mathématiques et génie in, École Polytechnique Montréal, Montreal, H3C 3A7, Québec, Canada

    Michel Gendreau

  2. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Pavillon André-Aisenstadt, Montreal, H3C 3J7, Québec, Canada

    Jean-Yves Potvin

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Pisinger, D., Ropke, S. (2010). Large Neighborhood Search. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_13

Download citation

Publish with us

Policies and ethics

Search

Navigation