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A Matheuristic Algorithm for Solving the Vehicle Routing Problem with Cross-Docking

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Learning and Intelligent Optimization (LION 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12096))

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Abstract

This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD. The aim is to find a set of routes to deliver single products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic approach that uses the routes of the local optima of an adaptive large neighborhood search (ALNS) as columns in a set-partitioning formulation of the VRPCD is designed. This matheuristic outperforms the state-of-the-art algorithms in solving a subset of benchmark instances.

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Acknowledgment

This research is supported by the Singapore Ministry of Education (MOE) Academic Research Fund (AcRF) Tier 1 grant.

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Authors and Affiliations

  1. Singapore Management University, 80 Stamford Road, Singapore, Singapore

    Aldy Gunawan & Audrey Tedja Widjaja

  2. Centre for Industrial Management/Traffic and Infrastructure, KU Leuven, Celestijnenlaan 300, Box 2422, Leuven, Belgium

    Pieter Vansteenwegen

  3. Department of Industrial Management, National Taiwan University of Science and Technology, 43, Sec. 4, Keelung Road, Taipei, 106, Taiwan

    Vincent F. Yu

Authors
  1. Aldy Gunawan
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  2. Audrey Tedja Widjaja
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  3. Pieter Vansteenwegen
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  4. Vincent F. Yu
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Corresponding author

Correspondence to Aldy Gunawan .

Editor information

Editors and Affiliations

  1. CARGO Lab., Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias S. Kotsireas

  2. Center for Applied Optimization, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Gunawan, A., Widjaja, A.T., Vansteenwegen, P., Yu, V.F. (2020). A Matheuristic Algorithm for Solving the Vehicle Routing Problem with Cross-Docking. In: Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2020. Lecture Notes in Computer Science(), vol 12096. Springer, Cham. https://doi.org/10.1007/978-3-030-53552-0_2

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