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Combinatorial Heuristics for Inventory Routing Problems

Author

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  • Ziye Tang

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Yang Jiao

    (Boeing Research & Technology, Tukwila, Washington 98108)

  • R. Ravi

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

We consider the deterministic inventory routing problem over a discrete finite time horizon. Given clients on a metric, each with daily demands that must be delivered from a depot and holding costs over the planning horizon, an optimal solution selects a set of daily tours through a subset of clients to deliver all demands before they are due and minimizes the total holding and tour routing costs over the horizon. In the capacitated case, a limited number of vehicles are available, where each vehicle makes at most one trip per day. Each trip from the depot is allowed to carry a limited amount of supply to deliver. We develop fast heuristics for both cases by solving a family of prize-collecting Steiner tree instances. Computational experiments show our heuristics can find near-optimal solutions for both cases and substantially reduce the runtime compared with a pure mixed integer programming formulation approach.

Suggested Citation

  • Ziye Tang & Yang Jiao & R. Ravi, 2022. "Combinatorial Heuristics for Inventory Routing Problems," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 370-384, January.
    • Handle: RePEc:inm:orijoc:v:34:y:2022:i:1:p:370-384
    DOI: 10.1287/ijoc.2021.1064
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    References listed on IDEAS

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