基于模式搜索的渴求函数法在多响应优化中的应用

渴求函数法是处理多响应参数优化的常用方法之一,它通过最大化总体渴求值获得因子的最佳水平组合.然而,随着因子个数和响应个数的增加,渴求函数往往变得多约束、多峰分布、高度非线性,传统的基于梯度的优化算法不适用.根据因子及响应个数等问题复杂程度不同,提出了以模式搜索算法为基础,用重叠等值线图或遗传算法设定模式搜索的起始点,对总体渴求函数进行寻优的新方法.算例验证了该方法的有效性.     

基于线性反馈策略的多阶段均值-方差投资组合优化

现有的多阶段投资组合优化问题普遍利用动态规划方法来进行求解,然而对于较为复杂的优化问题而言,该方法并不适用.针对此类复杂投资组合优化问题,文章首先在广义的多阶段均值一方差理论框架下,构建一类存在凸锥约束的投资组合优化模型.进而提出了一种有效的线性反馈策略,通过计算投资组合在各阶段财富值的收益和风险值,可将该动态随机控制问题转化为一个传统的凸优化问题.最后,在开环和闭环两种策略形式下通过数值仿真验证文章所提出反馈策略的有效性.结果表明,所提出的开环策略占优于已有的开环策略,且能够有效地拟合由动态规划所得精确投资策略.     

随机的库存-路径问题的机会约束规划模型与算法

随机需求下的库存-路径问题是一类复杂的组合优化问题.本文讨论了VMI背景下的库存-路径联合优化问题,构建了问题的机会约束规划模型,并将随机模拟、人工神经网络和遗传算法结合在一起,设计了求解问题的混合智能算法.实验表明算法性能良好.     

离散椭球分布下两阶段WCVaR风险利润优化模型及应用

本文研究随机变量非完全分布下的两阶段风险-利润优化问题。采用最坏情况下条件风险(Worst-case Conditional Value-at-Risk:WCVaR) 度量指标,在离散椭球分布下建立了两阶段WCVaR 约束下利润期望最大优化模型,运用优化对偶方法将复杂的Max-Min 结构化简,理论上证明了简化模型和原模型的同解性,以发电商电能分配组合优化为数值实例,验证了模型和计算方法的有效性。     

基于数据和整体的航空网络静态定价模型

建立了一个静态定价与座位分配联合模型.利用模型的性质,将问题简化为一个可分的凹规划模型.特别地,在一个三航段网络上,从模型的网络流形式推出最优目标函数具有良好的性质,并对模型的最优价格决策进行了灵敏度分析.最后给出了一个双枢纽网络上对细分产品定价的算例.     

基于可用度的装备售后保障系统控制策略研究

基于客户端装备系统可用度,构建一个由备件库存和服务台组成的闭环装备保障系统,通过分析备件库存水平状态特征,推导出备件库存水平状态稳态概率分布,并计算出可用度等几个保障质量指标,建立了基于可用度目标约束的保障系统控制优化模型,采用遗传算法并通过数值仿真揭示出可用度性质和保障系统的运作管理策略.     

具有现实约束的不确定均值—机会投资组合决策

本文将风险资产的收益看作梯形不确定变量,用机会约束表示风险控制(投资组合的实际收益低于预设的收益这一事件成立的机率不超过某一置信水平)。考虑最小交易量限制、基数约束、交易成本、借款约束和上下界约束,文章提出不确定均值——机会投资组合优化模型。该模型为全整数规划,精确算法难以在规定时间求出最优解,特别是当模型的规模较大时精确算法很难求解,文章设计一个遗传算法进行求解。最后通过实证研究分别对遗传算法进行可行性分析,对最大损失、置信度水平和投资组合中资产数量进行灵敏性分析,从而验证了模型和算法的有效性。     

基于模糊遗传算法的自融资有效投资组合研究

在马克维茨投资组合的均值一方差模型框架下,给出限制投资数量的自融资投资组合优化模型.把预期收益率不等式约束转化为模糊约束,采用一种通过惩罚因子,对适应度函数进行修正的模糊遗传算法来求解模型.在理论上,这种算法能够将最优基因较完整地遗传到下一代,有效地避免了早熟现象,可以得到更好的适应度函数值.在实际应用中,对一具体自融资有效投资组合实例进行计算,结果表明:本文所提出的模糊遗传算法是可行的、有效的,具有更好的优化结果.     

多约束的多阶段积极投资组合模型及实证研究

构建投资组合时需要衡量其风险, 除了考虑组合本身的风险暴露, 还需考虑其相对基准组合的风险暴露. 再者, 确定组合权重时需要根据市场的规则加入合适的约束. 基于此, 为了较为完整地考虑现实投资组合面临的风险及交易约束, 将绝对风险(CVaR)和相对风险(跟踪误差)作为风险约束, 将交易成本、卖空限制和多元权值作为交易限制约束, 构建一个新的多阶段投资组合模型, 并利用动态规划和非线性优化方法进行求解. 最后, 利用上证50成分股中41只股票构建投资组合进行实证研究. 实证结果表明构建的多阶段投资组合模型能持续战胜基准组合且优于单阶段投资组合, 同时也表明模型考虑多元权值约束具有现实意义.     

基于损失厌恶的Littlewood容量控制准则

在传统的航空收益管理单航段两种票价等级的容量控制准则—Littlewood准则容量控制模型基础上考虑有限理性决策中的损失厌恶特性和参照依赖属性,以盈亏均衡点为基点,引入具有损失厌恶特征的效用函数,在Kahneman和Tversky预期理论(Prospect Theory,PT)的框架下,给出单航段两种票价等级的不同损失厌恶程度下高等级座位保护水平的计算公式.理论分析和数值算例均表明,随着损失厌恶程度的增加,在其他条件不变的前提下,高等级座位的保护水平将不断降低.     

Mathematical programming for network revenue management revisited

Mathematical programming models for airline seat inventory control provide booking limits and bid-prices for all itineraries and fare classes. E.L. Williamson [Airline network seat inventory control: methodologies and revenue impacts, Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, 1992] finds that simple deterministic approximation methods based on average demand often outperform more advanced probabilistic heuristics. We argue that this phenomenon is due to a booking process that includes nesting of the fare classes, which is ignored in the modeling phase. The differences in the performance between these approximations are studied using a stochastic programming model that includes the deterministic model as a special case. Our study carefully examines the trade-off between computation time and the aggregation level of demand uncertainty with examples of a multi-leg flight and a single-hub network.     

A comparison of different dispatching policies in two-warehouse inventory systems for deteriorating items over a finite time horizon

This paper considers a two-warehouse inventory problem for deteriorating items with a constant demand rate over a finite time horizon. A modified first-in-first-out dispatching policy is first proposed, and a new two-warehouse inventory model based on this dispatching policy is developed. The results of this model are then compared with those of other models based on classical dispatching policies, such as the last-in-first-out, modified last-in-first-out and first-in-first-out dispatching policies. We also prove the existence and uniqueness of the optimal solutions for the models considered. Finally, a numerical example is presented to illustrate the results, and several key conditions are derived for comparing the general cases of these four models.     

基于数字仿真的收益管理整合决策

对航空公司收益管理进行机票定价和座位存量分配的整合研究。应用计算机仿真算法动态构造民航收益管理系统中的需求预测模型,并根据航班收益最大化原则,确定价格与座位存量分配,根据需求变化实时调整价格和座位存量。仿真运算结果显示,该算法可以使航空公司不同航班收益比固定价格提高2%以上。     

基于设备状态监控的单级备件库存系统补货策略研究

本文研究一个周期性订货的多设备同备件库存系统,将备件库存策略与设备状态监控相结合,讨论了存在设备状态监控情形下的备件库存策略。针对设备状态自然腐蚀过程和人 为修复过程的复合过程,运用一个新的马尔科夫概率转移矩阵对设备需求概率进行刻画,并在此基础上给出静态订货模型和状态监控下的动态订货模型的最优订货策略。通过对比以上两种订货策略优缺点,本文提出一种新的启发式订货策略: 基于关键状态的订货策略模型。该策略可以有效降低对全部设备实行动态监控的信息成本,且成本节省优于静态订货策略,对于企业的现实问题有着较好的指导意义。     

Single and multi-period optimal inventory control models with risk-averse constraints

This paper presents some convex stochastic programming models for single and multi-period inventory control problems where the market demand is random and order quantities need to be decided before demand is realized. Both models minimize the expected losses subject to risk aversion constraints expressed through Value at Risk (VaR) and Conditional Value at Risk (CVaR) as risk measures. A sample average approximation method is proposed for solving the models and convergence analysis of optimal solutions of the sample average approximation problem is presented. Finally, some numerical examples are given to illustrate the convergence of the algorithm.     

On a Double Porosity Model of Fractured-Porous Reservoirs Based on a Hybrid Flow Function

In this paper, a model of double porosity for a fractured porous medium using a combination of classical and gradient functions of mass transfer between the cracks and porous blocks in a weakly compressible single-phase fluid flow is considered. As compared to the wellknown models, the model with such a mass transfer function allows one to take into account the anisotropic properties of filtration in a more general form. The results of numerical tests for two- and three-dimensional model problems are presented. The computational algorithm is based on a finite element approximation with respect to space and a completely implicit approximation with respect to time.     

An efficient code for the minimization of highly nonlinear and large residual least squares functions

A new code for solving the unconstrained least squares problem is given, in which a Quasi-NEWTON approximation to the second order term of the Hessian is added to the first order term of the GAUSS-NEWTON method and a line search based upon a quartile model is used. The new algorithm is shown numerically to be more efficient on large residual problems than the GAUSS-NEWTON method and a general purpose minimization algorithm based upon BFGS formula. The listing and the user's guide of the code is also given.     

Approximate Bézier curves by cubic LN curves

In order to derive the offset curves by using cubic Bézier curves with a linear field of normal vectors (the so-called LN Bézier curves) more efficiently, three methods for approximating degree n Bézier curves by cubic LN Bézier curves are considered, which includes two traditional methods and one new method based on Hausdorff distance. The approximation based on shifting control points is equivalent to solving a quadratic equation, and the approximation based on L₂ norm is equivalent to solving a quartic equation. In addition, the sufficient and necessary condition of optimal approximation based on Hausdorff distance is presented, accordingly the algorithm for approximating the degree n Bézier curves based on Hausdorff distance is derived. Numerical examples show that the error of approximation based on Hausdorff distance is much smaller than that of approximation based on shifting control points and L₂ norm, furthermore, the algorithm based on Hausdorff distance is much simple and convenient.     

Finding the optimal CSP inventory level for multi-echelon system in Air Force using random effects regression model

Determining the optimal inventory level of CSP (concurrent spare parts) is crucial at the time of acquisition of new aircrafts. Most of the existing optimal CSP models do not take into account the time varying characteristics of CSP even though their demand rates are sensitive to such variation. In this paper, we introduce the CSP inventory model using a two stage approach. At the first stage, we use a random effects model to predict the expected demand of CSP in a multi-echelon system consisting of depot and bases based on CSPs varying characteristics with time. At the second stage, we find the optimal inventory level of CSP by using the optimization algorithm with various constraints under limited budget. The study is expected to contribute to the Air Force establishing the optimal national defense procurement policy for CSP of aircrafts.