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Looking Ahead with the Pilot Method

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  • Stefan Voßs
  • Andreas Fink
  • Cees Duin

Abstract

The pilot method as a meta-heuristic is a tempered greedy method aimed at obtaining better solutions while avoiding the greedy trap by looking ahead for each possible choice. Repeatedly a master solution is modified; each time in a minimal fashion to account for best choices, where choices are judged by means of a separate heuristic result, the pilot solution. The pilot method may be seen as a meta-heuristic enhancing the quality of (any) heuristic in a system for heuristic repetition. Experiments show that the pilot method as well as similar methods can behave quite competitively in comparison with well-known and accepted meta-heuristics. In this paper we review some less known results. As a higher time complexity is usually associated with repetition, we investigate a simple short-cut policy to reduce the running times, while retaining an enhanced solution quality. Furthermore, we report successful experiments that incorporate a distinguishing feature of the pilot method, which is the extension of neighborhoods into “local” search, creating tabu search hybrids. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Stefan Voßs & Andreas Fink & Cees Duin, 2005. "Looking Ahead with the Pilot Method," Annals of Operations Research, Springer, vol. 136(1), pages 285-302, April.
    • Handle: RePEc:spr:annopr:v:136:y:2005:i:1:p:285-302:10.1007/s10479-005-2060-2
    DOI: 10.1007/s10479-005-2060-2
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    5. Consoli, S. & Darby-Dowman, K. & Mladenovic, N. & Moreno Pérez, J.A., 2009. "Greedy Randomized Adaptive Search and Variable Neighbourhood Search for the minimum labelling spanning tree problem," European Journal of Operational Research, Elsevier, vol. 196(2), pages 440-449, July.
    6. Goodson, Justin C. & Thomas, Barrett W. & Ohlmann, Jeffrey W., 2017. "A rollout algorithm framework for heuristic solutions to finite-horizon stochastic dynamic programs," European Journal of Operational Research, Elsevier, vol. 258(1), pages 216-229.
    7. Jorge E. Mendoza & Bruno Castanier & Christelle Guéret & Andrés L. Medaglia & Nubia Velasco, 2011. "Constructive Heuristics for the Multicompartment Vehicle Routing Problem with Stochastic Demands," Transportation Science, INFORMS, vol. 45(3), pages 346-363, August.
    8. Raffaele Cerulli & Ciriaco D'Ambrosio & Domenico Serra & Carmine Sorgente, 2024. "The Budgeted Labeled Minimum Spanning Tree Problem," Mathematics, MDPI, vol. 12(2), pages 1-22, January.
    9. de Souza, Mauricio C. & Martins, Pedro, 2008. "Skewed VNS enclosing second order algorithm for the degree constrained minimum spanning tree problem," European Journal of Operational Research, Elsevier, vol. 191(3), pages 677-690, December.
    10. Jorge Oyola & Halvard Arntzen & David L. Woodruff, 2017. "The stochastic vehicle routing problem, a literature review, Part II: solution methods," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 349-388, December.
    11. Raka Jovanovic & Abdelkader Bousselham & Stefan Voß, 2018. "Partitioning of supply/demand graphs with capacity limitations: an ant colony approach," Journal of Combinatorial Optimization, Springer, vol. 35(1), pages 224-249, January.
    12. Carvalho, A.S. & Captivo, M.E. & Marques, I., 2020. "Integrating the ambulance dispatching and relocation problems to maximize system’s preparedness," European Journal of Operational Research, Elsevier, vol. 283(3), pages 1064-1080.
    13. Fabian Schäfer & Manuel Walther & Dominik G. Grimm & Alexander Hübner, 2023. "Combining machine learning and optimization for the operational patient-bed assignment problem," Health Care Management Science, Springer, vol. 26(4), pages 785-806, December.
    14. Amadeu Coco & João Júnior & Thiago Noronha & Andréa Santos, 2014. "An integer linear programming formulation and heuristics for the minmax relative regret robust shortest path problem," Journal of Global Optimization, Springer, vol. 60(2), pages 265-287, October.
    15. Höller, Holger & Melián, Belén & Voí, Stefan, 2008. "Applying the pilot method to improve VNS and GRASP metaheuristics for the design of SDH/WDM networks," European Journal of Operational Research, Elsevier, vol. 191(3), pages 691-704, December.
    16. GALARZA MONTENEGRO, Bryan David & SÖRENSEN, Kenneth & VANSTEENWEGEN, Pieter, 2023. "A demand-responsive feeder service with a maximum headway at mandatory stops," Working Papers 2023001, University of Antwerp, Faculty of Business and Economics.

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