Combinatorial Heuristics for Inventory Routing Problems

Ziye Tang (), Yang Jiao () and R. Ravi ()
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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

INFORMS Journal on Computing, 2022, vol. 34, issue 1, 370-384

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.

Keywords: inventory routing problem; prize-collecting Steiner tree; heuristics; large neighborhood search (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:34:y:2022:i:1:p:370-384

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