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

Research output: Contribution to journalArticle


Research units

  • RMIT University


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.


Original languageEnglish
Pages (from-to)494-524
JournalInternational Transactions in Operational Research
Issue number1
Publication statusPublished - Feb 2020
MoE publication typeA1 Journal article-refereed

    Research areas

  • stochastic programming, decomposition methods, Lagrangian duality, penalty-based method, Gauss-Seidel method

ID: 18522052