论文标题
基于模糊的C均基于随机服务网络设计捆绑的场景
Fuzzy C-means-based scenario bundling for stochastic service network design
论文作者
论文摘要
具有不确定需求的随机服务网络设计由一组方案表示，可以建模为大规模的两阶段随机混合组合程序（SMIP）。进行性对冲算法（PHA）是解决所得SMIP的分解方法。通过根据方案束而不是各个场景分解，可以通过分解PHA的计算性能得到极大的增强。基于捆绑的分解的核心是将场景分为捆绑的方法。在本文中，我们提出了一种模糊的基于C均值的方案捆绑方法来解决此问题。与其完全的捆绑会员资格，这通常是在诸如K均值之类的现有场景捆绑策略中，场景在每个捆绑包中都有部分成员资格，并且可以分配给我们方法中的多个捆绑包。
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposing according to scenario bundles instead of individual scenarios. At the heart of bundle-based decomposition is the method for grouping the scenarios into bundles. In this paper, we present a fuzzy c-means-based scenario bundling method to address this problem. Rather than full membership of a bundle, which is typically the case in existing scenario bundling strategies such as k-means, a scenario has partial membership in each of the bundles and can be assigned to more than one bundle in our method.
