不确定条件下的含存储时间有限的FlwoShop生产调度

针对企业中的不确定性因素 ,研究了不确定条件下的 Flow Shop生产调度问题 ,建立了基于模糊规划理论的模糊处理时间下的含存储时间有限型中间储罐的 Flow Shop的调度模型 ,将“中间值最大隶属度”算法从线性推广到非线性的调度模型中来 ,将模糊的优化问题转换为普通的优化问题 ,最后结合模拟退火算法 ( SA)进行优化求解 ,仿真结果证明了采用该算法的可行性     

输出/输入比值优势与DEA有效

进一步讨论了多目标规划模型 ( vp)中的 Pareto有效与 DEA有效的关系 ,得到两个重要结论 ,藉此无须求解 DEA模型就能判断某些 Pareto有效 DMU的 DEA有效性。     

基因表达式编程在组合预测建模中的应用

介绍一种利用基因表达式编程的方法来自动生成非线性函数的组合预测模型,并进行误差估计分析, 改变过去只依靠各个子方法的简单线性相加,不能很好地反映非线性真实世界的传统组合预测建模方法.通过对我国CPI的真实历史数据验证, 验证结果表明: 与传统的ARIMA,灰色GM(1,1), BP神经网络和线性组合预测四种方法对比,基因表达式编程建立的组合预测模型所预测的数据准确度明显提高.     

整数规划的旋转矢量法

针对整数规划问题的求解,改造了基本旋转矢量方法中的旋转因子和收缩因子。旋转因子的选取保证了矢量旋转过程中矢径长度不变,矢径的收缩策略的选取能保证最大范围地搜索解空间。多点旋转矢量法采用多矢量同时旋转的思想,在算法实施中基于优胜劣汰的原则引入了矢径舍弃系数和种群保留系数两个控制参数,极大地提高了计算效率和求解精度。最后,通过整数规划算例验证了该方法的有效性,表明对于维数较高的整数规划问题效果也很好。     

Optimal purchasing process for electricity and renewable energy credits with price and demand uncertainty

This paper optimizes the electricity and renewable energy credit (REC) purchasing process for energy distribution. Electricity is traded in deregulated time-sequential markets at fluctuating prices. Optimal electricity purchasing under price and demand uncertainty is a challenging task for electricity distributors, and the recently implemented renewable portfolio standards (RPS) further complicate the purchasing process. Government regulatory decisions concerning the RPS require distributors to purchase corresponding certificates, namely RECs, equivalent to a certain percentage of their electricity sales. This paper formulates and optimizes the joint purchasing process for electricity and RECs. It also analyzes the effect of RPS policy on electricity distributors.     

标识数学规划模型及其建模

通过深入分析数学规划建模语言存在的问题,提出了标识数学规划模型的概念,使得数学规划模型的代数形式与其自然语言的模型文档统一在同一框架中．该模型在明确了数学规划模型的层次结构和物理语义的同时,支持不同分量的建模语言,并使不同建模语言对决策用户透明．其中标识数学规划模型的二维形式建立了模型管理与关系数据库管理之间的联系．     

A HYBRID METHOD FOR LINEAR PROGRAMMING

1.IntroductionItiswellknownthatsinceKaxmarkax[1]proposedthefirstinferiorpoilltmethod,manyexpertshavebeeninvolvedtoimproveandcompletethismethod.ThereisalotofprogressinthisareaandmanyinteriorpointmethodsaredevelOPed;theinterestedreaderisreferredtotheexcellentbooksofhoandPllthenpura[2]andNesterovandNemirovskiil3].TheseiDteriorpointmethodscanbeclassifiedroughlyintothreecategories:(i)potentialreduction,(n)affinescaling,and(iii)path-following.Theaffine-scalingmethodisshowntoworkwellinpracticebyta…     

一种区间数线性规划的满意解

针对目标函数和约束条件均为区间数的线性规划问题,提出了一种基于模糊约束满意度的求解方法,把区间数线性规划卖座经为确定型的一般参数规划问题来求解,决策者可以根据自己的主观判断和客观情况,对目标函数优化水平和约束条件满意水平作出不同的估计,从而得到在相应情况下的满意解,使决策过程更具柔性。     

正负靶心灰靶决策模型

对灰靶决策模型进行了进一步的拓展研究.定义理想最优及理想最劣方案分别为灰靶的正负靶心,在综合考虑方案与正负靶心的距离,将靶心距作为向量在空间分析的基础上定义了综合靶心距.由各方案靶心距大小及指标权重的不确定性分析的基础上构建目标规划,并由此确定指标权重.由此建立了正负靶心灰靶决策模型.最后以实例说明了所提出的灰靶决策模型的合理性及算法的有效性.     

Modeling for UAV resource scheduling under mission synchronization

Unmanned aerial vehicle (UAV) resource scheduling means to allocate and aggregate the available UAV resources depending on the mission requirements and the battlefield situation assessment. In previous studies, the models cannot reflect the mission synchronization; the targets are treated respectively,which results in the large scale of the problem and high computational complexity. To overcome these disadvantages, a model for UAV resource scheduling under mission synchronization is proposed, which is based on single-objective non-linear integer programming. And several cooperative teams are aggregated for the target clusters from the available resources. The evaluation indices of weapon allocation are referenced in establishing the objective function and the constraints for the issue. The scales of the target clusters are considered as the constraints for the scales of the cooperative teams to make them match in scale. The functions of the intersection between the "mission time-window" and the UAV "arrival time-window" are introduced into the objective function and the constraints in order to describe the mission synchronization effectively. The results demonstrate that the proposed expanded model can meet the requirement of mission synchronization, guide the aggregation of cooperative teams for the target clusters and control the scale of the problem effectively.