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5540.pdf

🔗 Source: optimization-online.org

📊 Size: 584 KB

📄 Pages: 25 pages

⬇️ Downloads: 163

Summary

Researchers propose a scenario tree decomposition approach to obtain bounds for risk-averse mixed-integer multi-stage stochastic programming problems with a dynamic mean-CVaR risk measure, which can be applied to a wide range of problems without requiring special structural properties like convexity and linearity. The approach involves partitioning the sample space into subspaces called groups and solving the problem for scenarios in each group, yielding lower bounds through different convolutions of mean-CVaR risk measures and upper bounds through optimal solutions of group subproblems. Computational experiments on a multi-stage lot sizing problem demonstrate that the obtained bounds are tight, requiring reasonable CPU times, and the proposed algorithm yields 2.58 times stronger bounds than a commercial solver while requiring less than 5.45 times the CPU time for the same optimality gaps.

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Description

Researchers propose a scenario tree decomposition approach to obtain bounds for risk-averse mixed-integer multi-stage stochastic programming problems with a...

Technical Information

File Format: PDF
File Size: 584 KB
Pages: 25
Language: EN
Total Downloads: 163
Last Updated: 4 weeks ago

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