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GRASP: Greedy Randomized Adaptive Search Procedures

In: Search Methodologies

Author

Listed:
  • Mauricio G. C. Resende

    (AT&T Labs Research)

  • Celso C. Ribeiro

    (Universidade Federal Fluminense)

Abstract

GRASP is a multi-start metaheuristic for combinatorial optimization problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. An intensification strategy based on path-relinking is frequently used to improve solution quality and to reduce computation times by exploring elite solutions previously found along the search. This chapter describes the basic components of GRASP, successful implementation strategies, and effective hybridizations with path-relinking and other metaheuristics. We also list some tricks to be used in the quest for good implementations. The bibliography is enriched by an account of relevant applications and by links to surveys, software, and additional sources of material.

Suggested Citation

  • Mauricio G. C. Resende & Celso C. Ribeiro, 2014. "GRASP: Greedy Randomized Adaptive Search Procedures," Springer Books, in: Edmund K. Burke & Graham Kendall (ed.), Search Methodologies, edition 2, chapter 0, pages 287-312, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6940-7_11
    DOI: 10.1007/978-1-4614-6940-7_11
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    Citations

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    Cited by:

    1. Fernando Stefanello & Vaneet Aggarwal & Luciana S. Buriol & Mauricio G. C. Resende, 2019. "Hybrid algorithms for placement of virtual machines across geo-separated data centers," Journal of Combinatorial Optimization, Springer, vol. 38(3), pages 748-793, October.
    2. Musmanno, Leonardo M. & Ribeiro, Celso C., 2016. "Heuristics for the generalized median graph problem," European Journal of Operational Research, Elsevier, vol. 254(2), pages 371-384.
    3. Alexis Robbes & Yannick Kergosien & Virginie André & Jean-Charles Billaut, 2022. "Efficient heuristics to minimize the total tardiness of chemotherapy drug production and delivery," Flexible Services and Manufacturing Journal, Springer, vol. 34(3), pages 785-820, September.
    4. Anders Reenberg Andersen & Thomas Jacob Riis Stidsen & Line Blander Reinhardt, 2020. "Simulation-Based Rolling Horizon Scheduling for Operating Theatres," SN Operations Research Forum, Springer, vol. 1(2), pages 1-26, June.
    5. Jone R. Hansen & Kjetil Fagerholt & Magnus Stålhane & Jørgen G. Rakke, 2020. "An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships," Journal of Heuristics, Springer, vol. 26(6), pages 885-912, December.
    6. Ana Anokić & Zorica Stanimirović & Đorđe Stakić & Tatjana Davidović, 2021. "Metaheuristic approaches to a vehicle scheduling problem in sugar beet transportation," Operational Research, Springer, vol. 21(3), pages 2021-2053, September.
    7. Gómez-Lagos, Javier E. & González-Araya, Marcela C. & Soto-Silva, Wladimir E. & Rivera-Moraga, Masly M., 2021. "Optimizing tactical harvest planning for multiple fruit orchards using a metaheuristic modeling approach," European Journal of Operational Research, Elsevier, vol. 290(1), pages 297-312.

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