Combining penalty‐based and Gauss–Seidel methods for solving stochastic mixed‐integer problems

Fabricio Oliveira, Jeffrey Christiansen, Brian Dandurand, Andrew Eberhard

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

Abstrakti

In this paper, we propose a novel decomposition approach (named PBGS) for stochastic mixed‐integer programming (SMIP) problems, which is inspired by the combination of penalty‐based Lagrangian and block Gauss–Seidel methods. The PBGS method is developed such that the inherent decomposable structure that SMIP problems present can be exploited in a computationally efficient manner. The performance of the proposed method is compared with the progressive hedging (PH) method, which also can be viewed as a Lagrangian‐based method for obtaining solutions for SMIP problems. Numerical experiments performed using instances from the literature illustrate the efficiency of the proposed method in terms of computational performance and solution quality.
AlkuperäiskieliEnglanti
Sivut494-524
JulkaisuInternational Transactions in Operational Research
Vuosikerta27
Numero1
DOI - pysyväislinkit
TilaJulkaistu - helmikuuta 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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