Combining penalty‐based and Gauss–Seidel methods for solving stochastic mixed‐integer problems
作者: F. OliveiraJ. ChristiansenB. DandurandA. Eberhard
作者单位: 1Department of Mathematical Sciences School of Science – RMIT University Melbourne VIC Australia
2 Systems Analysis Laboratory, Department of Mathematics and Systems Analysis Aalto University FI‐00076 AALTO Finland
刊名: International Transactions in Operational Research, 2020, Vol.27 (1), pp.494-524
来源数据库: Wiley Journal
DOI: 10.1111/itor.12525
关键词: Stochastic programmingDecomposition methodsLagrangian dualityPenalty‐based methodGauss–Seidel method
原始语种摘要: Abstract(#br)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.
全文获取路径: Wiley  (合作)

  • stochastic 随机的
  • penalty 罚款
  • solving 求解
  • decomposable 可分解
  • hedging 买现卖期
  • based 基于
  • duality 二重性
  • Gauss 高斯
  • method 方法
  • inherent 固有的