Fast constructive heuristics for the uncapacitated inventory routing problem

Miguel Ángel Marfurt Alarcón (), Lehilton Lelis Chaves Pedrosa () and Fábio Luiz Usberti ()
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Miguel Ángel Marfurt Alarcón: Institute of Computing
Lehilton Lelis Chaves Pedrosa: Institute of Computing
Fábio Luiz Usberti: Institute of Computing

Journal of Heuristics, 2025, vol. 31, issue 2, No 6, 31 pages

Abstract: 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.

Keywords: Approximation algorithms; Inventory routing; Linear programming; Heuristic algorithms; Branch-and-cut (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10732-025-09558-1

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