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Machine Learning Constructives and Local Searches for the Travelling Salesman Problem

In: Operations Research Proceedings 2021

Author

Listed:
  • Tommaso Vitali

    (Università della Svizzera Italiana)

  • Umberto Junior Mele

    (Università della Svizzera Italiana
    Dalle Molle Institute for Artificial Intelligence, IDSIA-SUPSI)

  • Luca Maria Gambardella

    (Università della Svizzera Italiana
    Dalle Molle Institute for Artificial Intelligence, IDSIA-SUPSI)

  • Roberto Montemanni

    (University of Modena and Reggio Emilia)

Abstract

The ML-Constructive heuristic is a recently presented method and the first hybrid method capable of scaling up to real scale traveling salesman problems. It combines machine learning techniques and classic optimization techniques. In this paper we present improvements to the computational weight of the original deep learning model. In addition, as simpler models reduce the execution time, the possibility of adding a local-search phase is explored to further improve performance. Experimental results corroborate the quality of the proposed improvements.

Suggested Citation

  • Tommaso Vitali & Umberto Junior Mele & Luca Maria Gambardella & Roberto Montemanni, 2022. "Machine Learning Constructives and Local Searches for the Travelling Salesman Problem," Lecture Notes in Operations Research, in: Norbert Trautmann & Mario Gnägi (ed.), Operations Research Proceedings 2021, pages 59-65, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-08623-6_10
    DOI: 10.1007/978-3-031-08623-6_10
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