期刊
COMPUTERS & OPERATIONS RESEARCH
卷 67, 期 -, 页码 48-62出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2015.09.005
关键词
Multistage stochastic integer optimization; Cluster Lagrangean problem; Subgradient method; Volume algorithm; Lagrangean progressive hedging algorithm; Dynamic constrained cutting plane scheme
类别
资金
- Basque Country Government, BETS UPV \ EHU Research and Teaching Unit (UFI) [IT-567-13]
- Spanish Ministry of Economy and Competitiveness [MTM2012-31514]
We present a Lagrangean Decomposition approach for obtaining strong lower bounds on minimizing medium to large scale multistage stochastic mixed 0-1 problems. The problem is represented by a mixture of the splitting representation up to a given stage, so-named break stage, and the compact representation for the other stages along the time horizon. The dualization of the nonanticipativity constraints for the variables up to the break stage results in a model that can be decomposed into a set of independent scenario cluster submodels. The nonanticipativity constraints for the 0-1 and continuous variables in the cluster submodels are implicitly satisfied. Four scenario cluster schemes are compared for Lagrangean multipliers updating such as the Subgradient Method, the Volume Algorithm, the Lagrangean Progressive Hedging Algorithm and the Dynamic Constrained Cutting Plane scheme. We have observed in randomly generated instances that the smaller the number of clusters, the stronger the lower bound provided for the original problem (even, frequently, it is the solution value). (C) 2015 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据