 Partial Benders Decomposition: General Methodology and Application to Stochastic Network DesignTeodor Gabriel Crainic (), Mike Hewitt (), Francesca Maggioni () and Walter Rei () Transportation Science, 2021, vol. 55, issue 2, 414-435 Abstract: Benders decomposition is a broadly used exact solution method for stochastic programs, which has been increasingly applied to solve transportation and logistics planning problems under uncertainty. However, this strategy comes with important drawbacks, such as a weak master problem following the relaxation step that confines the dual cuts to the scenario subproblems. In this paper, we propose a partial Benders decomposition methodology, based on the idea of including explicit information from the scenario subproblems in the master. To investigate the benefits of this methodology, we apply it to solve a general class of two-stage stochastic multicommodity network design models. Specifically, we solve the challenging variant of the model where both the demands and the arc capacities are stochastic. Through an extensive experimental campaign, we clearly show that the proposed methodology yields significant benefits in computational efficiency, solution quality, and stability of the solution process. Keywords: partial Benders decomposition; L-shaped algorithm; stochastic network design models (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:55:y:2021:i:2:p:414-435 Access Statistics for this article More articles in Transportation Science from INFORMS Contact information at EDIRC. |
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