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A Learning-Based Matheuristic for Stochastic Multicommodity Network Design

Author

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  • Fatemeh Sarayloo

    (Department of Computer Science and Operations Research, University of Montreal and the Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Quebec H3T 1J4, Canada)

  • Teodor Gabriel Crainic

    (Department of Analytics, Operations and Information Technologies, School of Management Sciences, University of Quebec at Montreal and CIRRELT, Montreal, Quebec H2X 3X2, Canada)

  • Walter Rei

    (Department of Analytics, Operations and Information Technologies, School of Management Sciences, University of Quebec at Montreal and CIRRELT, Montreal, Quebec H2X 3X2, Canada)

Abstract

This paper proposes a solution approach for the multicommodity capacitated fixed-charge network design problem with uncertain demand modeled as a two-stage stochastic program. The proposed learning-based matheuristic combines heuristic search techniques with mathematical programming. It provides a systematic approach to identifying structures of good-quality solutions by gradually considering scenarios and their influences on design decisions. Extensive computational experiments illustrate the efficiency of the proposed matheuristic in obtaining high-quality solutions with limited computational efforts.

Suggested Citation

  • Fatemeh Sarayloo & Teodor Gabriel Crainic & Walter Rei, 2021. "A Learning-Based Matheuristic for Stochastic Multicommodity Network Design," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 643-656, May.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:2:p:643-656
    DOI: 10.1287/ijoc.2020.0967
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    References listed on IDEAS

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    Cited by:

    1. Liu, Chuanju & Lin, Shaochong & Shen, Zuo-Jun Max & Zhang, Junlong, 2023. "Stochastic service network design: The value of fixed routes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 174(C).

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