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Automatic instantiation of a Variable Neighborhood Descent from a Mixed Integer Programming model

Author

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  • Adamo, Tommaso
  • Ghiani, Gianpaolo
  • Guerriero, Emanuela
  • Manni, Emanuele

Abstract

In this paper we describe the automatic instantiation of a Variable Neighborhood Descent procedure from a Mixed Integer Programming model. We extend a recent approach in which a single neighborhood structure is automatically designed from a Mixed Integer Programming model using a combination of automatic extraction of semantic features and automatic algorithm configuration. Computational results on four well-known combinatorial optimization problems show improvements over both a previous model-derived Variable Neighborhood Descent procedure and the approach with a single automatically-designed neighborhood structure.

Suggested Citation

  • Adamo, Tommaso & Ghiani, Gianpaolo & Guerriero, Emanuela & Manni, Emanuele, 2017. "Automatic instantiation of a Variable Neighborhood Descent from a Mixed Integer Programming model," Operations Research Perspectives, Elsevier, vol. 4(C), pages 123-135.
    • Handle: RePEc:eee:oprepe:v:4:y:2017:i:c:p:123-135
    DOI: 10.1016/j.orp.2017.09.001
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    Cited by:

    1. Sadeghi, Parisa & Rebelo, Rui Diogo & Ferreira, José Soeiro, 2021. "Using variable neighbourhood descent and genetic algorithms for sequencing mixed-model assembly systems in the footwear industry," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. Franco Peschiera & Robert Dell & Johannes Royset & Alain Haït & Nicolas Dupin & Olga Battaïa, 2021. "A novel solution approach with ML-based pseudo-cuts for the Flight and Maintenance Planning problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 635-664, September.

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