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Fast constructive heuristics for the uncapacitated inventory routing problem

Author

Listed:
  • Miguel Ángel Marfurt Alarcón

    (Institute of Computing)

  • Lehilton Lelis Chaves Pedrosa

    (Institute of Computing)

  • Fábio Luiz Usberti

    (Institute of Computing)

Abstract

The inventory routing problem (IRP) poses a significant optimization challenge across various industries. This paper focuses on the uncapacitated IRP, by introducing fast constructive heuristics integrating insights from approximation algorithms, particularly rounding techniques in linear programming (LP). The proposed heuristics efficiently deliver effective solutions, providing advantages over methods such as branch-and-cut and metaheuristics. Methodologically, we emphasize scalability, subjecting our algorithms to rigorous stress tests with larger instances. Computational experiments, utilizing 420 instances, demonstrate the effectiveness and scalability of our heuristics, notably those tailored to specific problem variants, achieving an average gap of 2.2%. Our work underscores the effectiveness of leveraging approximation algorithms for the uncapacitated IRP, with future directions aimed at enhancing heuristics for broader real-world applicability, including the capacitated version of the IRP.

Suggested Citation

  • Miguel Ángel Marfurt Alarcón & Lehilton Lelis Chaves Pedrosa & Fábio Luiz Usberti, 2025. "Fast constructive heuristics for the uncapacitated inventory routing problem," Journal of Heuristics, Springer, vol. 31(2), pages 1-31, June.
    • Handle: RePEc:spr:joheur:v:31:y:2025:i:2:d:10.1007_s10732-025-09558-1
    DOI: 10.1007/s10732-025-09558-1
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