English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The use of Lagrangian cuts proves effective in enhancing the lower bound ofthe master problem within the execution of benders-type algorithms,particularly in the context of two-stage stochastic programs. However, even theprocess of generating a single Lagrangian cut is notably time-intensive. Inlight of this challenge, we present a novel framework that integratesLagrangian cut generation with an adaptive partition-based approach, therebymitigating this time-related drawback to a considerable extent. Furthermore, wealso discuss the dominance relationship between the generated partition-basedLagrangian cut and the Lagrangian cut for the original problem. To provideempirical evidence of our approach's efficacy, we undertake an extensivecomputational study encompassing instances involving even up to a thousandscenarios. The results of this study conclusively demonstrate the superiorityand efficiency of the proposed methodology.

ADPBA: Efficiently generating Lagrangian cuts for two-stage stochastic integer programs