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Stage- and scenario-wise Fenchel decomposition for stochastic mixed 0-1 programs with special structure

机译:具有特殊结构的随机混合0-1程序的阶段和场景范式Fenchel分解

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摘要

Solving stochastic integer programs (SIN) is generally difficult. This paper considers a comparative study of stage- and scenario-wise Fenchel decomposition (FD) for two-stage SIPs with special structure. The standard FD approach is based on stage-wise or Benders' decomposition. This work derives a scenario FD method based on decomposing the SIP problem by scenario and performs a computational study of the two approaches. In particular, two algorithms are studied, stage-wise FD (ST-FD) and scenario-wise FD (SC-FD) algorithms. The algorithms use FD cuts generated based on the scenario subproblem under each decomposition setting to iteratively recover (partially) the convex hull of integer points in the neighborhood of the optimal solution. The L-shaped method is used to solve the LP relaxation of the SIP problem in the ST-FD algorithm, while the progressive hedging algorithm (PHA) is used in the SC-FD algorithm. Computational results on knapsack test instances demonstrate the viability of both approaches towards solving large instances in reasonable amount of time and outperforming a direct solver in most cases. Overall, the ST-FD algorithm provides the best performance in our experiments. (C) 2015 Elsevier Ltd. All rights reserved.
机译:解决随机整数程序(SIN)通常很困难。本文考虑对具有特殊结构的两阶段SIP进行阶段和场景方式的Fenchel分解(FD)的比较研究。标准FD方法基于阶段分解或Benders分解。这项工作推导了一种基于场景分解SIP问题的场景FD方法,并对这两种方法进行了计算研究。尤其是,研究了两种算法,即阶段FD(ST-FD)和场景方式FD(SC-FD)算法。该算法使用在每个分解设置下根据场景子问题生成的FD割,来迭代(部分地)恢复最优解附近的整数点的凸包。 L形方法用于解决ST-FD算法中SIP问题的LP松弛问题,而SC-FD算法则使用渐进式套期保值算法(PHA)。背包测试实例的计算结果表明,这两种方法都可以在合理的时间内解决大型实例并在大多数情况下优于直接求解器。总体而言,ST-FD算法在我们的实验中提供了最佳性能。 (C)2015 Elsevier Ltd.保留所有权利。

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