FMS的模型参考自适应调度

用运筹学研究生产线调度问题已有几十年的历史,但由此得出的调度策略的实时性很差.1986年自动控制工作者提出了“PMS动态自适应计算机调”一词.令人感到十分新奇.可惜.从未见到任何有关的论文.本文研究FMS的模型参考自适应调度.本文采用我们提出的考虑了有限缓冲器容量的极大代数模型.环境的影响反映于对象参数的摄动,本文采用我们提出的摄动计算方法.调度器的设计采用次梯度法,以求解连续变量和离散变量混合的优化问题,附有数字实例,计算和仿真表明:FMS的模型参考自适应调度是可行的,是值得继续深入研究的。     

随机环境下带有私人信息的单机能力分配策略

本文研究随机环境下考虑私人信息的单机能力分配问题.该问题中各部门的单位产品收益、需求信息及加工设备各时段的能力为私人信息,且各时段中产品的加工时间为随机变量.本文采用设备方与各部门协商的方法对问题进行求解,首先利用随机规划理论将能力分配问题清晰化,然后利用拉格朗日松弛和泰勒级数展开方法进行协商机制设计,接着给出基于偏转次梯度方法的协商参数更新法则,最后综合上述过程给出最终的问题求解算法.数值算例验证了算法的有效性并分析了关键参数对能力分配结果的影响.     

基于凸集论的稠密视差图优化算法

针对计算机视觉领域中立体匹配算法在图像无纹理区产生的误匹配问题,提出以全变差函数为凸约束集,使用次梯度投影法,对通过初始视差变换后的立体图像对之间的像素误差函数和视差二次平滑因子形成的严格凸函数进行求解,寻求立体图像对之间的最优视差图。实验结果表明,该方法不仅能保留初始视差图的边缘,而且减少了由先前立体匹配算法求得的视差图在无纹理区产生的误匹配点,取得了良好的视差图优化效果。     

Production scheduling under uncertainty of an open-pit mine using Lagrangian relaxation and branch-and-cut algorithm

ABSTRACT

The life-of-mine optimization of open pit mine production scheduling under geological uncertainty is a computationally intensive process. Production scheduling determines the optimal extraction sequence by maximizing net present value (NPV). In this paper, an algorithm is proposed to schedule an open pit mine under geological uncertainty, where instead of solving the whole problem at once, the production schedule is generated by sequentially solving sub-problems. The sub-gradient method is used to generate the upper bound solution of a Lagrangian relaxed sub-problem. If the upper bound relaxed solution is infeasible, a mixed integer programming is applied to the latter solution. The algorithm is validated by solving six problems and is compared to the linear relaxation of the original production scheduling problem. The results show that the proposed algorithm generates a solution that is very close to optimal, with less than a 3% optimality gap. An application at a copper mine, where geological uncertainty is quantified with geostatistical simulations of the related orebody, shows that all constraints are satisfied and an 11% higher NPV is generated when compared to the corresponding deterministic equivalent of the proposed approach, while a 26% higher NPV is generated compared to a common conventional industry approach.     

Cost-effective selection and multi-period scheduling of pavement maintenance and rehabilitation strategies

An optimization methodology is developed for determining the most cost-effective maintenance and rehabilitation (M&R) activities for each pavement section in a highway pavement network, along an extended planning horizon. A multi-dimensional 0–1 knapsack problem with M&R strategy-selection and precedence-feasibility constraints is formulated to maximize the total dollar value of benefits associated with the selected pavement improvement activities. The solution approach is a hybrid dynamic programming and branch-and-bound procedure. The imbedded-state approach is used to reduce multi-dimensional dynamic programming to a one-dimensional problem. Bounds at each stage are determined by using Lagrangian optimization to solve a relaxed problem by means of a sub-gradient optimization method. Tests for the proposed solution methodology are conducted using typical data obtained from the Texas Department of Transportation.     

Stochastic sub-gradient algorithm for distributedoptimization with random sleep scheme

In this paper, we consider a distributed convex optimization problem of a multi-agent system with the global objective function as the sum of agents’ individual objective functions. To solve such an optimization problem, we propose a distributed stochastic sub-gradient algorithm with random sleep scheme. In the random sleep scheme, each agent independently and randomly decides whether to inquire the sub-gradient information of its local objective function at each iteration. The algorithm not only generalizes distributed algorithms with variable working nodes and multi-step consensus-based algorithms, but also extends some existing randomized convex set intersection results. We investigate the algorithm convergence properties under two types of stepsizes: the randomized diminishing stepsize that is heterogeneous and calculated by individual agent, and the fixed stepsize that is homogeneous. Then we prove that the estimates of the agents reach consensus almost surely and in mean, and the consensus point is the optimal solution with probability 1, both under randomized stepsize. Moreover, we analyze the algorithm error bound under fixed homogeneous stepsize, and also show how the errors depend on the fixed stepsize and update rates.     

P2P模式下产消者交易模型建立与仿真验证

随着新电改环境下售电侧放开,越来越多的产消者资源愿意参与到电力市场中以减少自身的用电开支,如何在P2P（peer-to-peer）交易模式下制定产消者购售电模型成为一个亟待解决的问题。首先,以含光伏（photovoltaic, PV）,微型燃气轮机和固定负荷资源组成的多个产消者集群为研究对象,建立了产消者资源约束模型。其次,采用拉格朗日对偶分解原理和次梯度法以产消者运行成本最小为目标制定了基于P2P市场交易模式的产消者购售电计划。在保证用户的信息安全的同时实现了产消者之间的P2P电能共享。最后通过算例验证了所提策略的合理性和有效性。     

应用非线性互补方法的电力市场均衡分析

利用一种求解混合非线性互补问题的非线性互补方法，分析具有输电网络约束的电力市场模型的供应函数Nash均衡问题．该方法利用每个策略发电商优化问题的Karush-Kuhn-Tucker(KKT)条件将均衡问题表述为一个混合的非线性互补问题；然后采用某些具有特殊性质的非线性互补函数，将求解．混合的非线性互补问题转化为求解一组非线性代数方程．该方程组中包含不光滑的函数，因此采用一种基于次梯度的改进的Levenberg-Marquardt算法对其求解．算例分析表明了该方法的有效性。     

一种改善小波变换模极大值重构信号的整体变分方法

在小波变换模极大值去噪原理的基础上,通过整体变分法估计模极大值以外、非零值的小波系数,然后经过小波逆变换重构信号.为求解这个极小化问题,采用了改进的次梯度方法.数值计算结果表明:我们提出的方法有效抑制了小波变换中固有的伪Gibbs现象,重构信号的边缘、脉冲位置都十分准确,信噪比也得到明显改善.