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Enhancing and extending the classical GRASP framework with biased randomisation and simulation

Author

Listed:
  • Daniele Ferone
  • Aljoscha Gruler
  • Paola Festa
  • Angel A. Juan

Abstract

Greedy Randomised Adaptive Search Procedure (GRASP) is one of the best-known metaheuristics to solve complex combinatorial optimisation problems (COPs). This paper proposes two extensions of the typical GRASP framework. On the one hand, applying biased randomisation techniques during the solution construction phase enhances the efficiency of the GRASP solving approach compared to the traditional use of a restricted candidate list. On the other hand, the inclusion of simulation at certain points of the GRASP framework constitutes an efficient simulation–optimisation approach that allows to solve stochastic versions of COPs. To show the effectiveness of these GRASP improvements and extensions, tests are run with both deterministic and stochastic problem settings related to flow shop scheduling, vehicle routing, and facility location.

Suggested Citation

  • Daniele Ferone & Aljoscha Gruler & Paola Festa & Angel A. Juan, 2019. "Enhancing and extending the classical GRASP framework with biased randomisation and simulation," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(8), pages 1362-1375, August.
    • Handle: RePEc:taf:tjorxx:v:70:y:2019:i:8:p:1362-1375
    DOI: 10.1080/01605682.2018.1494527
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    Citations

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    Cited by:

    1. Laura Calvet & Rocio de la Torre & Anita Goyal & Mage Marmol & Angel A. Juan, 2020. "Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review," Administrative Sciences, MDPI, vol. 10(3), pages 1-23, July.
    2. Mohammad Peyman & Pedro J. Copado & Rafael D. Tordecilla & Leandro do C. Martins & Fatos Xhafa & Angel A. Juan, 2021. "Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems," Energies, MDPI, vol. 14(19), pages 1-26, October.
    3. Arnau, Quim & Barrena, Eva & Panadero, Javier & de la Torre, Rocio & Juan, Angel A., 2022. "A biased-randomized discrete-event heuristic for coordinated multi-vehicle container transport across interconnected networks," European Journal of Operational Research, Elsevier, vol. 302(1), pages 348-362.
    4. Julio C. Londoño & Rafael D. Tordecilla & Leandro do C. Martins & Angel A. Juan, 2021. "A biased-randomized iterated local search for the vehicle routing problem with optional backhauls," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 387-416, July.
    5. Di Puglia Pugliese, Luigi & Ferone, Daniele & Festa, Paola & Guerriero, Francesca, 2020. "Shortest path tour problem with time windows," European Journal of Operational Research, Elsevier, vol. 282(1), pages 334-344.
    6. Yagmur S. Gök & Silvia Padrón & Maurizio Tomasella & Daniel Guimarans & Cemalettin Ozturk, 2023. "Constraint-based robust planning and scheduling of airport apron operations through simheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 795-830, January.
    7. Angel A. Juan & Peter Keenan & Rafael Martí & Seán McGarraghy & Javier Panadero & Paula Carroll & Diego Oliva, 2023. "A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 831-861, January.

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