Large Neighborhood Search

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


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.


Travel Salesman Problem Hamiltonian Path Large Neighborhood Repair Method Vehicle Rout Problem With Time Window 


  1. 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)CrossRefGoogle Scholar
  2. 2.
    Ahuja, R.K., Orlin, J.B., Sharma, D.: New neighborhood search structures for the capacitated minimum spanning tree problem. Technical Report 99–2, 1999Google Scholar
  3. 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. 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)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Brueggemann, T., Hurink, J.L.: Matching based exponential neighborhoods for parallel machine scheduling. Technical Report Memorandum No. 1773, (2005)Google Scholar
  7. 7.
    Brueggemann, T., Hurink, J.L.: Two exponential neighborhoods for single machine scheduling. Technical Report Memorandum No. 1776, 2005Google Scholar
  8. 8.
    Brueggemann, T., Hurink, J.: Two very large-scale neighborhoods for single machine scheduling. OR Spectr. 29, 513–533 (2007)CrossRefGoogle Scholar
  9. 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, 2005Google Scholar
  10. 10.
    Caseau, Y., Laburthe, F., Silverstein, G.: A meta-heuristic factory for vehicle routing problems. Lect. Notes Comput. Sci. 1713, 144–159 (1999)CrossRefGoogle Scholar
  11. 11.
    Cordeau, J.-F., Laporte, G., Pasin, F., Ropke, S.: Scheduling technicians and tasks in a telecommunications company. J. Scheduling (2010) ForthcomingGoogle Scholar
  12. 12.
    Cornuejols, G., Naddef, D., Pulleyblank, W.R.: Halin graphs and the traveling salesman problem. Math. Program. 26, 287–294 (1983)CrossRefGoogle Scholar
  13. 13.
    De Franceschi, R., Fischetti, M., Toth, P.: A new ILP-based refinement heuristic for vehicle routing problems. Math. Program. 105, 471–499 (2006)CrossRefGoogle Scholar
  14. 14.
    Dowsland, K.A.: Nurse scheduling with tabu search and strategic oscillation. Eur. J. Oper. Res. 106, 393–407 (1998)CrossRefGoogle Scholar
  15. 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)CrossRefGoogle Scholar
  16. 16.
    Flood, M.M.: The traveling salesman problem. Oper. Res. 4(1), 61–75 (1956)CrossRefGoogle Scholar
  17. 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, 2006Google Scholar
  18. 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, 1998Google Scholar
  19. 19.
    Glover, F.: Ejection chains, reference structures, and alternating path algorithms for the traveling salesman problem. Technical Report, 1992Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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 2005Google Scholar
  22. 22.
    Goel, A.: Vehicle scheduling and routing with driver’s working hours. Transport. Sci. (2009) ForthcomingGoogle Scholar
  23. 23.
    Goel, A., Gruhn, V.: A general vehicle routing problem. Eur. J. Oper. Res. 191(3), 650–660 (2008)CrossRefGoogle Scholar
  24. 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. 25.
    Hansen, P., Mladenović, N.: Variable neighborhood search: Principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)CrossRefGoogle Scholar
  26. 26.
    Hurink, J.: An exponential neighborhood for a one machine batching problem. OR-Spektr. 21, 461–476 (1999)CrossRefGoogle Scholar
  27. 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 1997Google Scholar
  28. 28.
    Laborie, P., Godard, D.: Self-adapting large neighborhood search: Application to single-mode scheduling problems. Technical Report TR-07-001, ILOG, 2007Google Scholar
  29. 29.
    Lin, S., Kernighan, B.: An effective heuristic algorithm for the traveling salesman problem. Oper. Res. 21, 498–516 (1973)CrossRefGoogle Scholar
  30. 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)CrossRefGoogle Scholar
  31. 31.
    Mladenovic, N., Hansen, P.: Variable neighborhood search. Comput. Oper. Res. 24, 1097–1100 (1997)CrossRefGoogle Scholar
  32. 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, GermanyGoogle Scholar
  33. 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)CrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 35.
    Perron, L.: Fast restart policies and large neighborhood search. In: Proceedings of CP-AI-OR’2003, Montreal, Canada 2003Google Scholar
  36. 36.
    Perron, L., Shaw, P.: Parallel large neighborhood search. In: Proceedings of RenPar’15, La Colle sur Loup, France 2003Google Scholar
  37. 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)CrossRefGoogle Scholar
  38. 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)CrossRefGoogle Scholar
  39. 39.
    Pisinger, D., Ropke, S.: A general heuristic for vehicle routing problems. Comput. Oper. Res. 34(8), 2403–2435 (2007)CrossRefGoogle Scholar
  40. 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 2007Google Scholar
  41. 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. 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. 43.
    Ropke, S.: Parallel large neighborhood search – a software framework. In: Proceedings of MIC 2009: The VIII Metaheuristics International Conference. Hamburg, GermanyGoogle Scholar
  44. 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)CrossRefGoogle Scholar
  45. 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)CrossRefGoogle Scholar
  46. 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. 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)CrossRefGoogle Scholar
  48. 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. 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)CrossRefGoogle Scholar
  50. 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. 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)CrossRefGoogle Scholar
  52. 52.
    Thompson, P.M.: Local search algorithms for vehicle routing and other combinatorial problems. PhD Thesis, Operations Research Center, MIT (1988)Google Scholar
  53. 53.
    Thompson, P.M., Psaraftis, H.N.: Cyclic transfer algorithms for multivehicle routing and scheduling problems. Oper. Res., 41 (1993)Google Scholar
  54. 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. 55.
    Winter, P.: Steiner problem in Halin networks. Discrete Appl. Math., 17, 281–294 (1987)CrossRefGoogle Scholar
  56. 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)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Management EngineeringTechnical University of DenmarkLyngbyDenmark
  2. 2.Department of TransportTechnical University of DenmarkLyngbyDenmark

Personalised recommendations