Abstract
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 language | English |
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Pages (from-to) | 494-524 |
Number of pages | 31 |
Journal | International Transactions in Operational Research |
Volume | 27 |
Issue number | 1 |
DOIs | |
Publication status | Published - Jan 2020 |
MoE publication type | A1 Journal article-refereed |
Keywords
- stochastic programming
- decomposition methods
- Lagrangian duality
- penalty-based method
- Gauss-Seidel method