唐家山堰塞湖泄洪问题研究

研究了唐家山堰塞湖的泄洪规律,并对其蓄水、溃坝、泛洪等三个过程进行了模型分析.     

唐家山堰塞湖泄洪问题的研究

研究的是唐家山地震次生灾害引发的堰塞湖问题.首先对数字高程地图进行等高图像分析求解了堰塞湖不同高程水位对应的湖区面积,建立了蓄水量体积与堰塞湖水位高程的离散化模型,然后建立了神经网络模型和多元线性回归模型研究了北川降雨量与堰塞湖入库流量的关系,继而求解得到不同降雨量下每日堰塞湖水位高程.在研究泄洪过程时,首先通过对泄洪过程和溃坝过程内在机理的研究分别建立了正交多项式逼近模型和仿真模型得到溃坝时的溃口流量随时间变化的关系,继而分析求解得到溃坝时其他参数随时间变化的关系.针对淹没区的问题,综合数字高程地图和行政区域地图,利用数字地图计算了洪水到达各被淹没区域的时间,淹没范围,以便于确定撤离方案.     

泄洪设施修建计划的数学模型

某乡政府计划解决防汛水利设施建设问题,即通过开挖小型排洪沟与修建新的泄洪河道来满足防汛需要.针对该乡的泄洪设施修建计划,主要研究以下三个问题:(1)给出同时开挖给定的8条小型排洪沟和新建一条给定的泄洪河道的最优修建方案;(2)已知该乡各村之间新建泄洪河道的长度,给出一个在各村之间互通的另一新泄洪河道的网络修建计划,使之达到可泄洪量100万立方米/小时;(3)当新泄洪河道网络修建完后,安排人员进行维护工作,研究维护人员在各村留宿的概率分布.在费用最省的目标下,建立了问题(1)和问题(2)的数学规划模型,并得到泄洪设施的最优修建方案.应用Markov链及转移概率矩阵等知识,建立了问题(3)的等概率和非等概率的两种数学模型,并得知维护人员在各村留宿的概率分布是稳定的.     

多系统服务中心的多技能服务人员调度问题研究

研究具有多系统的服务中心的多技能服务人员的调度问题.通过解决雇用哪些类型的服务人员,各类人员雇用多少,人员如何在各系统中转移以及在什么时候休息等问题,使得服务中心在满足一定的服务水平下的雇用和转移成本最小.并且建立了该复杂系统的整数规划模型,对于大规模问题提出了基于列生成法的启发式算法,列举了一个算例来简单说明此方法的基本步骤.     

基于移动Agent技术的网格负载均衡调度方法

为了实现网格计算资源的动态自适应性管理和负载均衡调度,将移动Agent技术引入网格资源管理,并提出了一种基于Agent的网格资源调度模型.在该模型基础上,采用遗传克隆算法解决网格计算中的任务调度问题.仿真实验表明该算法不仅充分发挥了Agent的智能性、自主性,还具有良好的扩展性,提高了网格资源调度的效率.     

基于改进模拟植物生长算法的集装箱码头插船调度优化

为解决临时接受计划外船舶到港作业的插船调度问题,建立了综合考虑港口安排插船作业的成本最小优化模型,将模拟植物生长算法(PGSA)改进后进行求解。经过对实际案例进行计算分析后表明,所建模型和算法可以有效解决上述问题并取得了较好结果。为验证算法的有效性,同时引入遗传算法进行计算对比,结果显示经改进的PGSA在求解过程中具有较好的收敛速度与精确度。采用本文建立的模型和算法能够快速解决临时插船的调度调整问题,为集装箱码头在特殊情况下泊位调度优化提供了解决问题的思路和方法。     

改进的萤火虫算法求解具有学习退化效应的JSP问题

作业车间调度问题是典型的NP难题,在生产调度领域具有很高的研究价值.一种更为符合实际的作业车间调度问题是加工机器具有学习退化效应,它能够为生产者安排生产计划提供借鉴.为了可以更好的解决具有学习退化效应的作业车间调度问题,本文提出了改进的萤火虫算法(IFA),即在基本的萤火虫算法基础上增加了局部寻优的过程,并融合了布谷鸟算法中生物移动的莱维分布特点.通过MATLAB模拟分析,IFA能够更快速的收敛到JSP的最优解.最后,本文分析了不同学习率与退化效应因子组合对目标函数求解的影响.     

有顾客需求变动的车辆调度干扰管理研究

为解决来自顾客需求变动所引发的物流配送干扰问题,提出基于干扰管理思想构建扰动恢复策略与方案.应用虚拟单车场实现了车辆调度扰动恢复问题转化,提出了车辆调度扰动恢复策略以及扰动度量方法,以作为车辆调度干扰管理建模的基础;对有顾客需求变动的车辆调度干扰管理问题,分析了顾客需求变动造成的扰动并进行了辨识;建立了相应的干扰管理模型,并提出了归一化处理办法对VRPTW问题进行有效兼容;结合干扰管理模型的特点,改进了基于顾客的编码表示方法;根据干扰管理思想,设计了遗传算法对干扰管理模型进行了求解.最后通过实验验证了模型与算法的有效性.     

邮政运输中邮路的规划和邮车调度问题的研究

以邮政运输网络中运输效益最优为目标,建立了分步规划的图论模型.运用Floyd算法、Kruskal算法对模型进行分步求解并逐步优化,通过Matlab、Lingo、SPSS软件求解,提出三种优化邮路、降低邮车调度成本的方法.模型对解决邮路问题、单旅行商、多旅行商等相关问题具有普遍适用性,可以推广到点数更多TSP的问题.     

基于WSD算法的水资源调度综合策略

为更好地解决水资源短缺的问题,逐层深入构建了预测模型、量化模型和调度决策模型。首先,利用改进的灰色预测方法分别建立可用水资源储量预测模型和水资源需求预测模型,结合实际情况,得到水资源调度、去盐碱化技术等单位成本的量化方法;然后,利用新提出的WSD算法以及AHP方法,从经济、环境以及自然3方面进行综合分析,建立了适应各地区实际情况的可持续发展水战略;最后,基于提出的模型和算法进行仿真,得出了一套解决2025年中国各地区水资源短缺问题的可行的水资源调度方案。     

Finite element analysis of flood discharge atomization based on water–air two-phase flow

Flood discharge atomization is a phenomenon of water fog diffusion caused by the discharge of water from a spillway structure, which brings strong wind and heavy rainfall. These unnatural winds and rainfall are harmful for the safe operation of hydropower stations with high water heads. Compared to the method of prototype observations, physical models and mathematical models, which are semi-theoretical and semi-empirical, numerical simulation methods have the advantage of being not limited by a similar scale and are more economical. A finite element model is presented to simulate flood discharge atomization based on water–air two-phase flow in this paper. Equations governing flood discharge atomization are composed of partial differential equations of mass and momentum conservation laws with unknowns for pressure, velocity and the water concentration. The finite element method is used to solve the governing equations by adopting appropriate solution strategies to increase the convergence and numerical stability. Then, the finite element model is applied to a practical project, the Shuibuya hydropower station, which experienced a flood discharge in 2016. Simulation results show that the proposed model can simulate flood discharge atomization with efficient convergence and numerical stability in three dimensions, and good agreement was observed between numerical simulations and prototype observational data. Based on the simulation results, the mechanism of flood discharge atomization was analyzed.     

Reservoir flood control operation based on chaotic particle swarm optimization algorithm

Reservoir flood control operation is a complex engineering optimization problem with a large number of constraints. In order to solve this problem, a chaotic particle swarm optimization (CPSO) algorithm based on the improved logistic map is presented, which uses the discharge flow process as the decision variables combined with the death penalty function. According to the principle of maximum eliminating flood peak, a novel flood control operation model has been established with the goal of minimum standard deviation of the discharge flow process. At the same time, a piecewise linear interpolation function (PLIF) is applied to deal with the constraints for solving objective function. The performance of the proposed model and method is evaluated on two typical floods of Three Gorges reservoir. In comparison with existing models and other algorithms, the proposed model and algorithm can generate better solutions with the minimal flood peak discharge and the maximal peak-clipping rate for reservoir flood control operation.     

A dynamic multi-objective optimization model with interactivity and uncertainty for real-time reservoir flood control operation

Despite the successes of both multi-objective optimization and uncertainty handling techniques in reservoir flood control operation, no work has been done yet on developing and investigating dynamic multi-objective optimization models for this problem. In this work, a dynamic multi-objective optimization model with interactivity and uncertainty was developed for the real-time reservoir flood control operation. Accordingly, a dynamic multi-objective optimization algorithmic framework with two newly designed change reaction strategies was proposed for solving the proposed dynamic model. Following the proposed algorithmic framework, any evolutionary multi-objective optimization algorithm can be converted into a dynamic optimizer. After investigating the difficulty variation of the proposed dynamic model, the effectiveness and robustness of the proposed algorithmic framework have been validated based on experiential studies on two typical floods of Ankang reservoir.     

Non-probabilistic Bayesian update method for model validation

Model validation is the principal strategy to evaluate the accuracy and reliability of computational simulations. A systematic model validation procedure including uncertainty quantification, model update and prediction is described based on a non-probabilistic interval model. The crucial technical challenge in model validation is limited data, thus the non-probabilistic interval model is adopted to describe uncertain parameters. To establish the model update formula, the concepts of the interval escape rate and interval coverage rate are first described. Then, not only can the possibility of failure be estimated but also the credibility of the possibility of failure based on the proposed model validation method. The data in the validation experiment are used to update the credibility of each interval model, while the data from the accreditation experiment are used to conduct a final check of the validated models. To demonstrate that the proposed method can be applied to model validation problems successfully, a validation benchmark, the static frame challenge problem, is implemented. In addition, a practical aviation structure engineering validation problem is described. The results of these two validation problems show the feasibility and effectiveness of the proposed model validation method. The theoretical framework proposed in this paper is also suitable for model validation of computational simulations in other research fields.     

Functional regression modeling via regularized Gaussian basis expansions

We consider the problem of constructing functional regression models for scalar responses and functional predictors, using Gaussian basis functions along with the technique of regularization. An advantage of our regularized Gaussian basis expansions to functional data analysis is that it creates a much more flexible instrument for transforming each individual’s observations into functional form. In constructing functional regression models there remains the problem of how to determine the number of basis functions and an appropriate value of a regularization parameter. We present model selection criteria for evaluating models estimated by the method of regularization in the context of functional regression models. The proposed functional regression models are applied to Canadian temperature data. Monte Carlo simulations are conducted to examine the efficiency of our modeling strategies. The simulation results show that the proposed procedure performs well especially in terms of flexibility and stable estimates.     

Fuzzy facility location-allocation problem under the Hurwicz criterion

Facility location-allocation (FLA) problem has been widely studied by operational researchers due to its many practical applications. Many researchers have studied the FLA problem in a deterministic environment. However, the models they proposed cannot accommodate satisfactorily various customer demands in the real world. Thus, we consider the FLA problem with uncertainties. In this paper, a new model named α-cost model under the Hurwicz criterion is presented with fuzzy demands. In order to solve this model, the simplex algorithm, fuzzy simulations and a genetic algorithm are integrated to produce a hybrid intelligent algorithm. Finally, some numerical examples are presented to illustrate the effectiveness of the proposed algorithm.     

基于惩罚似然比的高维空间相关过程EWMA质量监控模型

变量选择控制图是高维统计过程监控的重要方法。针对传统变量选择控制图较少考虑高维过程空间相关性而造成监控效率低的问题,提出一种基于Fused-LASSO的高维空间相关过程监控模型。首先,利用Fused LASSO算法对似然比检验进行改进;然后,推导出基于惩罚似然比的监控统计量;最后,通过仿真模拟和真实案例分析所提监控模型的性能。仿真实验和真实案例均表明:在高维空间相关过程中,当相邻监控变量同时发生异常时,利用所提监控方法能够准确识别潜在异常变量,取得较好的监控效果。     

Integrated hydrodynamic model for simulation of river-lake-sluice interactions

In this paper, an integrated model for simulating the hydrodynamic process of river-lake-sluice (RLS) systems is presented. It includes a novel one-dimensional (1D) and two-dimensional (2D) coupling method called the coupling-zone iteration-correction (CZIC) method, and an improved numerical algorithm for the sluice problem. The 1D river-network model and the 2D lake model are coupled by establishing a coupling region, and iterative correction is carried out to ensure the accurate transfer of hydraulic parameters. The convergence conditions of the CZIC method are discussed theoretically, and the proper spatial step of the coupling zone is adopted according to different inflow conditions to ensure stable computation. In order to deal with the transition of flow regimes during the gate operation, a method for calculating the discharge capacity is presented. In addition, a general difference coefficient of the river reach is deduced for hydrodynamic calculation with sluices included. Simulations on open channels demonstrate that (1) simulated values of the CZIC method are consistent with the results of the full 2D model; (2) the sluice solving algorithm can stably handle the flow transition between the orifice flow and weir flow. Furthermore, the developed integrated model is applied to the middle and lower reaches of the Huaihe River, including the Hongze Lake and fifteen sluices. Numerical simulation results reproduced the hydrodynamic process during the flood season of 2007 accurately and efficiently. The errors of the present model are also compared with that of the MIKE model, and the results show that the proposed methods perform better than MIKE, especially in rising and flood periods. Therefore, it seems likely that the developed integrated model will work well in hydrodynamic modelling of large-scale complex RLS systems.