A Learning-Based Matheuristic for Stochastic Multicommodity Network Design

Fatemeh Sarayloo (), Teodor Gabriel Crainic () and Walter Rei ()
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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

INFORMS Journal on Computing, 2021, vol. 33, issue 2, 643-656

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.

Keywords: stochastic capacitated network design; uncertain multicommodity demand; two-stage formulation; matheuristic (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:2:p:643-656

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