Abstract
We propose a solution approach for stochastic network design problems with uncertain demands. We investigate how to efficiently use reduced cost information as a means of guiding variable fixing to define a restriction that reduces the complexity of solving the stochastic model without sacrificing the quality of the solution obtained. We then propose a matheuristic approach that iteratively defines and explores restricted regions of the global solution space that have a high potential of containing good solutions. Extensive computational experiments show the effectiveness of the proposed approach in obtaining high-quality solutions, while reducing the computational effort to obtain them.
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Acknowledgements
While working on this project, T.G. Crainic was Adjunct Professor with the Department of Computer Science and Operations Research, Université de Montréal. Partial funding for this project has been provided by the Natural Sciences and Engineering Council of Canada (NSERC), through its Discovery Grant program, and by the Fonds Québécois de la Recherche sur la nature et les technologies (FQRNT) through its strategic center infrastructure grant program.
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Sarayloo, F., Crainic, T.G. & Rei, W. A reduced cost-based restriction and refinement matheuristic for stochastic network design problem. J Heuristics 27, 325–351 (2021). https://doi.org/10.1007/s10732-020-09460-y
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DOI: https://doi.org/10.1007/s10732-020-09460-y