生鲜类物流配送网络选址-路径优化问题研究

为解决生鲜类物流配送网络选址-路径优化问题,构建了基于服务质量最优化、物流节点建造成本及物流运营服务成本最小化的多目标两层级物流配送网络选址-路径优化问题数学模型,并通过改进遗传算法求解最优方案.对遗传算法中的算子进行优化,解决了传统遗传算法求解过程中无法求得全局最优解以及易陷入局部最优解的现象.通过选取通州区部分区域为背景进行模型验证,得出优化后成本节约了15.71%,说明该模型具有良好的参考价值.     

基于动态规划的迭代算法设计方法

为了基于动态规划法设计求约束最优化问题(COPs)最优解的迭代算法,在避免使用"标记函数"和递归算法的前提下提出了两种求解模式,给出了设计求COPs最优解的迭代算法一般方法,并利用两个典型优化问题-最长公共子序列问题和矩阵链乘法问题,阐明了如何利用两种求解模式设计求COPs最优解的简捷迭代算法.     

求解多项式规划的一个全局最优化算法

提出一个求解带箱子约束的一般多项式规划问题的全局最优化算法, 该算法包含两个阶段, 在第一个阶段, 利用局部最优化算法找到一个局部最优解. 在第二阶段, 利用一个在单位球上致密的向量序列, 将多元多项式转化为一元多项式, 通过求解一元多项式的根, 找到一个比当前局部最优解更好的点作为初始点, 回到第一个 阶段, 从而得到一个更好的局部最优解, 通过两个阶段的循环最终找到问题的全局最优解, 并给出了算法收敛性分析. 最后, 数值结果表明了算法是有效的.     

风险中性零售商的合作广告策略与订货模型

考虑了由一个制造商与一个零售商构成的单期二阶段供应链是否进行合作广告的博弈问题.面对市场需求的不确定性,零售商从制造商处订购报童类型产品销售给消费者,零售商具有风险中性的行为特征.通过不合作广告与合作广告两种情形,制造商与零售商进Stackelberg主从博弈,得到了均衡解,比较后发现,合作广告下的最优解及利润总是优于不合作广告下的最优解和利润,告诉了上下游企业采用合作广告的广告策略.最后,通过数值算例,给出了需求敏感系数对最优决策的影响,同时也论证了有关结论.     

投票悖论概率计算的基本定理

多数偏好规则是求解群体最优化问题最重要和应用最广泛的规则之一.但是,在使用这一规则对给定的群体最优化问题寻求最优解中,有时会发生群体偏好循环排序的"投票悖论"现象,从而发生"投票悖论"现象的概率计算,便成为群体最优化中一个基本的研究课题.为此,引进群体在方案集上的"投票悖论排序剖面"和"投票悖论选型剖面"的概念.借助于这两个概念,建立了群体中每一个体对所有方案的偏好排序都可各具不同概率分布的一般情况下,群体对问题进行方案择优时,发生"投票悖论"概率计算的基本定理.于是,解决了求解群体最优化时发生"投票悖论"的概率计算研究中,长期未能得到彻底解决的基本问题.     

稳健资产负债管理最优化模型及实证分析

本文提出一种新的稳健资产负债模型最优化模型.该模型考虑了利率的不确定性对未来现金流、资金成本和资产收益率的影响.我们通过构建情景树反映未来的利率变化的情景结构.由于最优决策对利率的预测十分敏感,我们提出系数预测值可在一定误差范围内的稳健资产负债最优化模型.实证分析结果表明,从收益与风险均衡的角度看,稳健优化模型产生的保守解优于系数确定的优化模型产生的最优解.     

两种抽样方案的最优化设计

本文运用贝叶斯分析方法导出两类抽样方案中样本容量ｎ和合格判定数ｃ之间的最优关系,从而得到两种抽样方案的最优化设计方法     

三阶Runge-Kutta最优算法

本文利用最优化思想,根据一阶常微分方程数值解的收敛性和稳定性,引进最优化技术,确定最优系数,得到强稳定的三阶Runge-Kutta最优算法.     

一类系数为梯形模糊数的两层多随从线性规划模型

针对一类系数为梯形模糊数的两层多随从线性规划问题,利用模糊结构元理论定义了模糊结构元加权序,证明了一类系数为梯形模糊数的两层多随从线性规划问题的最优解等价于两层多随从线性规划问题的最优解.根据线性规划的对偶定理和互补松弛性质,得到了两层多随从线性规划模型的最优化条件.最后,利用两层多随从线性规划模型的最优化条件,设计了求解一类系数为梯形模糊数的两层多随从线性规划问题的算法,并通过算例验证了该方法的可行性和合理性.     

一个有经济意义的多目标规划模型的调整

本文在线性约束条件下 ,同时考虑三个目标函数的最优化 ,即线性函数、二次函数、分式函数 .对于已知的线性规划的最优基可行解 ,通过调整二次函数和分式函数中的系数向量和系数矩阵 ,使其成为这两个规划的最优解 .模型的改进有经济意义的解释     

A CELL-CENTERED ALE METHOD WITH HLLC-2D RIEMANN SOLVER IN 2D CYLINDRICAL GEOMETRY

This paper presents a second-order direct arbitrary Lagrangian Eulerian (ALE) method for compressible flow in two-dimensional cylindrical geometry.This algorithm has half-face fluxes and a nodal velocity solver,which can ensure the compatibility between edge fluxes and the nodal flow intrinsically.In two-dimensional cylindrical geometry,the control vol-ume scheme and the area-weighted scheme are used respectively,which are distinguished by the discretizations for the source term in the momentum equation.The two-dimensional second-order extensions of these schemes are constructed by employing the monotone up-wind scheme of conservation law (MUSCL) on unstructured meshes.Numerical results are provided to assess the robustness and accuracy of these new schemes.     

MAP 1, MAP2 /M/c retrial queue with guard channels and its application to cellular networks

In this paper, we investigate the impact of retrial phenomenon on loss probabilities and compare loss probabilities of several channel allocation schemes giving higher priority to hand-off calls in the cellular mobile wireless network. In general, two channel allocation schemes giving higher priority to hand-off calls are known; one is the scheme with the guard channels for hand-off calls and the other is the scheme with the priority queue for hand-off calls. For mathematical unified model for both schemes, we consider theMAP ₁,MAP ₂ /M/c/b, ∞ retrial queue with infinite retrial group, geometric loss, guard channels and finite priority queue for hand-off class. We approximate the joint distribution of two queue lengths by Neuts' method and also obtain waiting time distribution for hand-off calls. From these results, we obtain the loss probabilities, the mean waiting time and the mean queue lengths. We give numerical examples to show the impact of the repeated attempt and to compare loss probabilities of channel allocation schemes.     

城市防洪减灾决策方案优化研究

城市防洪减灾决策的成功与否直接影响着将来的洪水所造成的损失大小.如何应用自然、经济和社会信息,经超前分析以确保实施某一优化方案之后所造成的损失最小是防洪减灾决策的核心问题.本文深入地分析了城市防洪减灾决策方案优化的众多影响因素,在此基础上把模糊优选神经网络模型应用到城市防洪减灾方案优化中,并建立了城市防洪减灾方案优化模型,并通过实例验证了其有效性.     

Highly Accurate Numerical Schemes for Stochastic Optimal Control via FBSDEs

This work is concerned with numerical schemes for stochastic optimal control problems (SOCPs) by means of forward backward stochastic differential equations (FBSDEs). We first convert the stochastic optimal control problem into an equivalent stochastic optimality system of FBSDEs. Then we design an efficient second order FBSDE solver and an quasi-Newton type optimization solver for the resulting system. It is noticed that our approach admits the second order rate of convergence even when the state equation is approximated by the Euler scheme. Several numerical examples are presented to illustrate the effectiveness and the accuracy of the proposed numerical schemes.     

Semi‐implicit schemes for transient Navier–Stokes equations and eddy viscosity models

This study presents two computational schemes for the numerical approximation of solutions to eddy viscosity models as well as transient Navier–Stokes equations. The eddy viscosity model is one example of a class of Large Eddy Simulation models, which are used to simulate turbulent flow. The first approximation scheme is a first order single step method that treats the nonlinear term using a semi‐implicit discretization. The second scheme employs a two step approach that applies a Crank–Nicolson method for the nonlinear term while also retaining the semi‐implicit treatment used in the first scheme. A finite element approximation is used in the spatial discretization of the partial differential equations. The convergence analysis for both schemes is discussed in detail, and numerical results are given for two test problems one of which is the two dimensional flow around a cylinder. © 2008 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2009     

Receding Horizon Robust Control for Nonlinear Systems Based on Linear Differential Inclusion of Neural Networks

In this paper, we present a new receding horizon neural robust control scheme for a class of nonlinear systems based on the linear differential inclusion (LDI) representation of neural networks. First, we propose a linear matrix inequality (LMI) condition on the terminal weighting matrix for a receding horizon neural robust control scheme. This condition guarantees the nonincreasing monotonicity of the saddle point value of the finite horizon dynamic game. We then propose a receding horizon neural robust control scheme for nonlinear systems, which ensures the infinite horizon robust performance and the internal stability of closed-loop systems. Since the proposed control scheme can effectively deal with input and state constraints in an optimization problem, it does not cause the instability problem or give the poor performance associated with the existing neural robust control schemes.     

Algorithms for adaptive stochastic control for a class of linear systems

This paper is concerned with the control of linear, discrete-time, stochastic systems with unknown control gain parameters. Two suboptimal adaptive control schemes are derived: One is based on underestimating future control and the other is based on overestimating future control. Both schemes require little on-line computation and incorporate in their control laws some information on estimation errors. The performance of these laws is studied by Monte Carlo simulations on a computer. Two single-input, third-order systems are considered, one stable and the other unstable, and the performance of the two adaptive control schemes is compared with that of the scheme based on enforced certainty equivalence and the scheme where the control gain parameters are known.     

High performance computation for DNS/LES

The paper is focused on high-order compact schemes for direct numerical simulation (DNS) and large eddy simulation (LES) for flow separation, transition, tip vortex, and flow control. A discussion is given for several fundamental issues such as high quality grid generation, high-order schemes for curvilinear coordinates, the CFL condition for complex geometry, and high-order weighted compact schemes for shock capturing and shock–vortex interaction. The computation examples include DNS for K-type and H-type transition, DNS for flow separation and transition around an airfoil with attack angle, control of flow separation by using pulsed jets, and LES simulation for a tip vortex behind the juncture of a wing and flat plate. The computation also shows an almost linear growth in efficiency obtained by using multiple processors.     

Fourth order accurate compact scheme with group velocity control (GVC )

For solving complex flow field with multi-scale structure higher order accurate schemes are preferred. Among high order schemes the compact schemes have higher resolving efficiency. When the compact and upwind compact schemes are used to solve aerodynamic problems there are numerical oscillations near the shocks. The reason of oscillation production is because of non-uniform group velocity of wave packets in numerical solutions. For improvement of resolution of the shock a parameter function is introduced in compact scheme to control the group velocity. The newly developed method is simple. It has higher accuracy and less stencil of grid points     

Formulation and numerical solution of finite-level quantum optimal control problems

Optimal control of finite-level quantum systems is investigated, and iterative solution schemes for the optimization of a control representing laser pulses are developed. The purpose of this external field is to channel the system's wavefunction between given states in its most efficient way. Physically motivated constraints, such as limited laser resources or population suppression of certain states, are accounted for through an appropriately chosen cost functional. First-order necessary optimality conditions and second-order sufficient optimality conditions are investigated. For solving the optimal control problems, a cascadic non-linear conjugate gradient scheme and a monotonic scheme are discussed. Results of numerical experiments with a representative finite-level quantum system demonstrate the effectiveness of the optimal control formulation and efficiency and robustness of the proposed approaches.