Bad-scenario-set Robust Optimization Framework With Two Objectives for Uncertain Scheduling Systems

This paper proposes a robust optimization framework generally for scheduling systems subject to uncertain input data, which is described by discrete scenarios. The goal of robust optimization is to hedge against the risk of system performance degradation on a set of bad scenarios while maintaining an excellent expected system performance. The robustness is evaluated by a penalty function on the bad-scenario set. The bad-scenario set is identified for current solution by a threshold, which is restricted on a reasonable-value interval. The robust optimization framework is formulated by an optimization problem with two conflicting objectives. One objective is to minimize the reasonable value of threshold, and another is to minimize the measured penalty on the bad-scenario set. An approximate solution framework with two dependent stages is developed to surrogate the biobjective robust optimization problem. The approximation degree of the surrogate framework is analyzed. Finally, the proposed bad-scenario-set robust optimization framework is applied to a scenario job-shop scheduling system. An extensive computational experiment was conducted to demonstrate the effectiveness and the approximation degree of the framework. The computational results testified that the robust optimization framework can provide multiple selections of robust solutions for the decision maker. The robust scheduling framework studied in this paper can provide a unique paradigm for formulating and solving robust discrete optimization problems. © 2014 Chinese Association of Automation.

基于坏场景集的抗风险鲁棒调度模型

讨论了场景描述的不确定环境下的鲁棒调度模型.通过对传统不确定调度模型在追求优良性能的积极性和抗风险的保守性两方面对抗和均衡关系的透视和分析,建立了一种新的鲁棒调度模型.该模型的优化目标由平衡因子将期望性能和抗风险鲁棒度量组合而成.抗风险鲁棒度量基于坏场景集概念而定义,坏场景集中坏场景的数目可由一个基准性能来调节,当平衡因子或者基准性能变化时,构成一族鲁棒调度模型.一系列的定理阐明了本文提出的鲁棒调度模型族与传统不确定调度模型之间的关系,给出了该鲁棒调度模型有效的条件.仿真测试实验针对加工时间不确定的Job-shop调度问题进行,计算结果表明新模型在追求优良性能的积极性和抵抗风险的鲁棒性方面相对传统模型具有了更好的全面性和综合性,可以实现调度解在不同场景下的期望性能和抗风险鲁棒性的更好平衡.

坏场景集下含风电多源系统的改进鲁棒调度模型

针对不确定条件下含风电的多源电力系统运行的安全性与经济性的优化问题，提出基于坏场景集的改进鲁棒调度模型。首先采用坏场景集法来描述风电出力波动的随机性与恶劣性，然后基于现有的含平衡因子的均值．方差调度模型，针对其鲁棒度量提出改进，将预测风电出力场景下的调度成本纳入目标函数，建立包含风电、常规火电机组与储能系统的鲁棒调度模型，该模型兼顾系统经济性与抗风险性，并能给出可执行的调度方案。最后，通过对算例的仿真计算，对比分析所提改进模型与现有的均值．方差调度模型在经济性、鲁棒性及调度可行性方面的性能表现，验证该模型的优越性与可行性。所有仿真实验均由专业优化软件LINGO15编程实现。

电网大数据跨行业数据融合应用场景

电网智能化的深入发展,使得跨行业数据,即异构多源数据(heterogeneous multi source data,HMSD)量激增,且数据的规模与特点符合大数据的特征。HMSD中不良数据的大量存在,能显著降低电网规划准确性和安全性。如何增强不良数据的分析能力,提高分析准确性,成为目前亟待解决的问题。文章利用Metropolis接受准则对大数据HMSD进行分析,从配电网负荷、电网运行状态、电能质量检测等应用场景出发,构建电网大数据跨行业数据融合应用场景评价模型,并进行MATLAB仿真分析。结果显示,所构建模型对于不良数据的综合辨识能力较佳,能够在1min内准确识别HMSD中的不良数据,准确率可达95%以上,所以该模型适用于电网大数据跨行业数据融合分析。

上海世博TD-LTE演示网安全解决方案

本文基于上海世博TD-LTE演示网,简要介绍核心网SAE的建设情况及系统架构,并进一步探讨,核心网SAE的网络级安全保障机制,特别地,针对不同厂商核心网设备覆盖不同世博场馆的应用场景,本文介绍了一种在不同厂商核心网设备间保证数据卡入网鉴权兼容性的技术方案。最后,从业务安全性层面,提出了基于现网不良信息监控系统,部署TD-LTE演示网公网业务的安全性保障方案及建议。