多周期多种设备公用工程系统优化模型的改进及应用

本文提出了多周期多种设备公用工程系统改进的混合整数双线性优化模型,它含有两种优化变量和系统运行过程的离散动态约束,期望系统总设备投资(含设备折旧)与全周期运行操作费用之和最小。针对改进优化模型求解上的困难,给出将改进优化模型分解成有限多个关于连续变量的线性规划。论述了改进优化模型与分解模型的等价性以及两种模型的主要数学性质,并在此基础上提出了求解策略。最后将改进优化模型应用于某石化企业的蒸汽动力系统最优设计与运行优化集成实例。     

计及多重阻塞的电动汽车移动储能特性建模

为实现城市交通电力耦合系统在城市道路、充电设施、输电线路阻塞环境下的优化运行,提出了计及多重阻塞的动态交通电力流联合优化方法。首先,基于时空网络模型,提出了计及电动汽车移动、静止、充电、排队模式的队列时空网络模型,构建了适用于电动汽车的车辆调度模型,进而形成动态交通分配模型,以减少交通出行损失。其次,通过优化发电机组、储能等的出力和备用计划,计及城市电网安全、备用约束,构建了安全约束动态经济调度模型,以降低碳排放及发电成本。随后,形成多目标动态优化模型,并将其转换为混合整数凸二次规划问题。最后,在耦合IEEE-30、Sioux Falls系统中验证了所提模型的有效性。     

一类灰色动态投入产出优化模型的设计及其在农业经济发展规划中的应用

本文应用灰色系统理论和方法，把动态Ｌｅｏｎｔｉｅｆ投入产出模型和线性规划结合起来，建立一种灰色动态投入产出优化模型，在山东省农业经济发展规划定量研究中，应用该模型取得比较满意的结果．     

交通网络建设序列的动态规划方法

交通网络建设序列优化是交通规划中一个重要问题。文章对交通网络设计及其建设序列问题的研究现状进行了分析。按照网络建设中规划者和用户间的关系,以交通网络建设序列下的各阶段系统总费用作为上层规划,以各阶段的交通流用户平衡模型作为下层规划,建立了双层规划模型。并依照问题的特点,采用动态规划的求解方法进行探讨,而下层模型则采用了基于路径搜索的GP算法进行求解。并针对网络规划算例进行了计算,针对固定和变动客流OD两种情况下的结果进行了分析。计算的结果表明,问题的双层规划模型和动态规划求解算法能够为路网规划决策提供支持。     

动态系统的模糊关系建模算法及实现

模糊集理论是一种研究复杂系统的有效的定性方法，本文研究在动态系统建模中使用模糊集和语言变量的方法。文章首先给出了基于模糊关系的动态系统模型表达形式，进而导出了一种利用牛顿梯度下降的模糊系统建模算法。并就一个简单的例子验证了上述算法的有效性。     

基于复杂项目群优化选择的水电经济效益计算

针对目前已运行水电站经济效益计算存在的问题,本文提出了基于大系统分解协调模型选择水电站在不同运行时间的最优等效替代运行位置.考虑目前电力系统中正在逐步推行的两部制电价（容量电价与电量电价）,研究中分别计算了已运行水电站最优等效替代位置的边际容量成本和边际电量成本,并将其分别作为已运行水电站优化运行中各时段（刻）经济效益计算的容量价格与电量价格.以运行水电站经济效益最大作为优化准则,采用动态规划方法对其进行实时调度,从而实现其经济运行调度.文章给出的实例计算表明,本文提出的方法与模型是可行的.     

产业结构调整路径优化模型

从动态系统的角度,对产业结构调整过程进行研究.建立了产业结构调整过程优化决策模型,应用动态规划对该模型的求解给予讨论,并根据该模型的特殊性,提出简便算法.     

演化博弈与逻辑动态系统的优化控制

探讨演化博弈与逻辑动态系统的优化控制的关系.主要内容包括三个方面:1)讨论基于演化博弈的逻辑动态(控制)系统的建模,即如何从演化动态博弈导出多值逻辑系统的优化控制问题;2)多值逻辑系统在平均收益和带贴现因子的总收益两种性能指标下的优化控制的基本结论与算法;3)如何从逻辑动态系统的最优控制导出演化博弈的纳什均衡.使用的基本工具是矩阵的半张量积,基本方法是将逻辑动态系统转化为基于矩阵的离散时间动态系统和博弈策略的矩阵表示.     

一类离散事件动态系统极点分配的优化问题

应用极大代数作为数学工具,用系统矩阵的特征值法讨论了m × n-型离散事件动态系统极点分配的优化问题.给出了系统取得优化的条件.并证明了至少存在,n个具有最优极点分配的优化系统.     

船体不规则分段的动态空间调度算法

提出了一种基于遗传算法的针对船体不规则分段的动态空间调度方法。基于遗传算法和临界多边形算法直接对不规则分段进行优化调度，提高空间的利用率。首先，利用遗传算法产生多个可行的分段调度序列。然后，基于临界多边形算法及重心最低策略，根据分段调度序列一一确定各不规则分段的空间位置。充分考虑到空间调度问题所特有的动态性和时空关联性，同时以场地利用率和未延迟加工分段所占比例的综合加权和作为评价函数，实现了调度方案的全局优化。以江苏澄西船厂大型散货船的119个不规则分段作为实验数据，并通过多个算法的比较，发现该动态调度算法可以快速收敛得到较优解，并且在空间利用率和延迟分段指标上有了提高，最终可以得到比较理想的调度结果，证明了所提方法在动态空间调度问题上的有效性和优越性。     

中型水库系统运行管理随机动态决策模型的研究

本文针对中型水库系统优化调度中的难点问题，提出了相应的解决方法，由此给出简单易行的系统最优运行决策模型。     

Power series solution to the pendulum equation

This note gives a power series solution to the pendulum equation that enables to investigate the system in an analytical way only, i.e. to avoid numeric methods. A method of determining the number of the terms for getting a required relative error is presented that uses bigger and lesser geometric series. The solution is suitable for modelling the motion in spreadsheets up to the amplitude of 90°.     

Optimization of natural gas production by waterflooding

This paper discusses optimization of natural gas production using waterflooding of a gas reservoir. The functions governing the rate of gas withdrawal and the rate of water injection control the operation of the reservoir. These, together with volume and mass balance and the ideal gas law, give a simple system of ordinary differential equations modelling the reservoir. We use several physical and economic definitions of an optimal production rate. Under each definition, we establish the optimal controls. The relation between prices of water and natural gas and the optimal controls is discussed.Work performed under the auspices of the Energy Research and Development Administration.     

The impact of special days in call arrivals forecasting: A neural network approach to modelling special days

A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established, statistical methods, we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter, we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe’s leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods.     

Identification of a nonlinear circuit

The identification of a differential equation underlying a measured time series is a prerequisite for numerous types of applications. In the validation of a proposed parameterized model one often faces the dilemma that it is hard to decide whether possible discrepancies between the measured time series and the simulated model output are caused by an inappropriate model or by wrongly specified parameters in a correct type of model. We propose a combination of parametric modelling based on Bock's multiple shooting algorithm and nonparametric modelling based on optimal transformations as a strategy to test proposed models and if rejected suggest and test new ones. We exemplify this strategy on an experimental time series from a nonlinear chaotically oscillating circuit where we finally obtain an extremely accurate reconstruction of the observed attractor.     

Estimation for regression with infinite variance errors

This paper addresses the problem of modelling time series with nonstationarity from a finite number of observations. Problems encountered with the time varying parameters in regression type models led to the smoothing techniques. The smoothing methods basically rely on the finiteness of the error variance, and thus, when this requirement fails, particularly when the error distribution is heavy tailed, the existing smoothing methods due to [1], are no longer optimal. In this paper, we propose a penalized minimum dispersion method for time varying parameter estimation when a regression model generated by an infinite variance stable process with characteristic exponent α ε (1, 2). Recursive estimates are evaluated and it is shown that these estimates for a nonstationary process with normal errors is a special case.     

Mu-Synthesis for Magnetic Bearings of Flywheel

In this paper, the μ-Synthesis Control method is applied to control Active Magnetic Bearings (AMBs) Flywheel. The dynamics of the open-loop system is influenced by external disturbances, nonlinearities, uncertainties and signal limits. Thus, a design methodology, which covers the practical issues of optimal robust control like the modelling of uncertainties, selection of optimal weighting functions is presented. The goal of this paper is the experimental evaluation robust performance of the AMBs system. The laboratory stand with the high speed rotor supported magnetically was built and presented. The stable operation, good stiffness of the rotor suspension and robust performances of the closed-loop AMBs is reached. Finally, the success of the robust control is demonstrated through results of computer simulations and experimental results. The experimental results show the effectiveness of the control system as well as good vibrations reduction of the designed controllers. (© 2009 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Enriched workflow modelling and Stochastic Branch-and-Bound

Workflow systems provide means and techniques for modelling, designing, performing and controlling repetitive (business) processes. The quality of commercial workflow systems is usually determined to a large extent by their versatility and multi-purpose application. One of the current trends in improving workflow systems lies in enriching modelling methods and techniques in order to enlarge design alternatives.The need for such advanced methods is particularly apparent in those fields in which the process duration can be determined only vaguely, but whose completion schedules are at the same time strictly enforced by a highly competitive market by means of fines and penalties. The risk of an overrun has to be weighed against the expected costs and benefits of certain measures reducing turn-around time and their combinations. Because they can help to avoid such penalties—or, at least, keep any potential losses low by identifying critical subprocesses and evaluate appropriate measures—modelling and evaluation techniques are becoming essential features of workflow systems.Methodologically, we use Stochastic Branch-and-Bound as a technique for finding “optimal” bundles of measures. A numerical study shows the benefits of this meta-approach by means of five stepwise-developed decision scenarios requiring rich modelling. Petri nets as a modelling tool and Stochastic Branch-and-Bound as an optimization technique determine for multi-mode resource constrained workflows of varying complexity an optimal workforce strategy with respect to the number of workers and their qualification.     

A Successful Implementation of Locating Passive Cooling Facilities: The Case of Northern Thailand

This paper reports the actual implementation of a location-allocation study, which is one aspect of the overall Passive Cooling (Thailand) Project conducted in Northern Thailand since 1985. The goal has been to generate decision supports for ‘optimal’ placements of cooling facilities and the assignment of sites to these facilities as part of the post-harvest handling-chain operation of agricultural products. The basic questions addressed in this work are the issues of how many facilities to set up and where to locate them for maximum utility. Our selected approach is to use discrete location-allocation modelling, as, among other reasons, the problem at hand has a most natural network setting. Direct methods as well as efficient heuristics are examined, and sample numerical results are reported as illustrations of the continual implementation.     

具有年龄结构和约束的群落系统的最优收获

研究一类有年龄结构和相互作用的两种群构成的群落系统的最优收获问题,要求控制过程结束时的种群状态充分接近预先指定的年龄分布.证明了最优控制的存在性,运用Dubovitskii-Milyutin理论导出了最优性条件.这种处理方法为研究连续年龄分布下种群收获问题提供了统一框架.