基于不确定控制理论的最优策略研究

本文在不确定理论的框架下,研究一类带背景状态变量的最优控制模型.在乐观值准则下,利用不确定动态规划的方法,证明了不确定最优性原则,得到最优性方程.作为应用,求解一个固定缴费(DC)型养老金的最优投资策略问题,在乐观值准则下,以工资变量为背景状态变量,建立养老金模型.通过求解不确定最优性方程得到最优投资策略和最优支付率.     

多损失条件风险值的多层规划模型

对于多个损失函数,在给定的置信水平下,首先定义了不超过给定损失值的最小风险值(即Va R值)和基于权值的累积期望损失值(即CVa R损失值)概念,然后建立了一个多损失条件风险值的多层规划模型.该模型的目标是求各层多损失CVa R值达最小的最优策略,并证明了它等价于另一个较容易求解的多层规划模型.最后,给出了三级供应链中多产品的定价与订购的条件风险值模型(三层线性规划模型).     

乐观型二层随机规划逼近问题最优解集的上半收敛性

文章针对下层随机规划反馈的最优解不唯一,上层为单目标约束随机规划的一类乐观型二层随机规划逼近问题,构建了求解乐观型二层随机规划逼近最优解集上半收敛的理论框架.首先将乐观型二层随机规划等价转化为单层随机规划问题,通过逼近方法建立了无界可积函数在有限区域上以及全空间上的一致逼近定理,应用此结果给出了目标函数的连续收敛性和约束集的K-收敛性.其次利用上图收敛理论,得到了乐观型二层随机规划逼近最优解集的上半收敛性.该结论提供了乐观型二层随机规划逼近最优解集可以近似替代精确的最优解集的理论依据,结果表明离散化逼近方法是可行的、有效的、合理的.     

一种多随从双层条件风险值模型的等价性定理

多随从风险决策问题是供应链风险决策中普遍存在的问题,文章研究了风险厌恶下的多随从双层条件风险值模型,引入了多随从上下层决策的VaR损失值(最小风险值)和CVaR损失值(最小风险值对应的条件期望损失值或条件风险价值度量)概念,提出了一种风险厌恶下的多随从双层条件风险值模型,该模型的目标是求上下层的基于权值的多损失CVaR达最小的最优解,文章证明了它可以通过另一个较容易求解的双层规划模型获得最优解的等价性定理.     

基于二次B样条的时间最优轨迹规划

轨迹规划是机器人运动中的基本问题,文章给出带动力学限制的时间最优二次B样条轨迹的规划方法.算法首先搜索可见性图的对偶图得到初始折线路径.在此基础上可以求解带有避障条件的二次B样条拟合问题,到无碰撞光滑的运动轨迹.在此基础上,联合动力学限制建立新的时间最优模型,并用"Bang-Bang-Singular"控制策略求解得到运动轨迹.数值实验表明,文章方法可以求得符合动力学限制的时间最优运动路径.     

区间值函数的可微性及其在区间值规划中的应用

利用实值函数的全微分思想,讨论了区间值函数的可微性,建立了区间值函数的$D$-可微性的概念及其一些基本性质. 通过讨论无约束区间规划的最优性条件,给出了一类约束函数为实值函数的约束区间值规划问题取得最优解的必要条件. 同时给出了具有实值函数约束的凸区间值规划问题取得最优解的充分条件.     

多目标条件风险值的一种近似求解方法

本文研究了一种求解多目标条件风险值问题的近似方法,首先引入了多个损失函数在对应的置信水平下关于一个证券组合的α-VaR损失值,以及α-CVaR损失值概念．α-CVaR损失值表明了在给定的证券组合于置信水平对应的最小信用风险值的条件期望损失值,那么求出这样的最小条件期望损失值的模型构成了一个求解α-CVaR损失值的多目标问题,它的解就是最小条件期望损失值的有效证券组合,即Pareto弱有效解．为了求解它的Pareto弱有效解,我们引进了损失函数对应的优化问题(SCVaR),可以通过求解非线性规划问题(SCVaR)的最优解近似地刻画α—CVaR损失值,这样使得求解α-CVaR损失值变得容易．     

一种基于匈牙利算法的二次分配问题求解方法

二次分配问题(Quadratic assignment problem,QAP)属于NP-hard组合优化难题.二次分配问题的线性化及下界计算方法,是求解二次分配问题的重要途径.以Frieze-Yadegar线性化模型和Gilmore-Lawler下界为基础,详细论述了二次分配问题线性化模型的结构特征,并分析了Gilmore-Lawler下界值往往远离目标函数最优值的原因.在此基础上,提出一种基于匈牙利算法的二次分配问题对偶上升下界求解法.通过求解QAPLIB中的部分实例,说明了方法的有效和可行性.     

基于ADI格式的多尺度图像分解

针对文献[Xu R et al.,IEEE Trans.Biomed.Eng.,2014]的多尺度图像分解模型,该文提出了Alternating Direction Implicit (ADI)格式下的多尺度图像分解算法,并证明了在该模型下ADI格式的收敛性和稳定性,进一步,通过对不同图像的数值实验,验证了该文提出的算法具有更好的纹理提取效果.     

摩擦市场的均值-离差证券组合选择模型

本文给出了基于历史收益率数据的均值 -平均绝对离差型证券组合投资模型 .该模型采用收益的平均绝对离差作为风险的尺度 ,可以通过求解线性规划来获的摩擦市场 (如具有税收和交易费 )最优投资组合 ,避免了均值 -方差模型求解二次规划的复杂性 .     

基于交互式模糊规划方法的可持续闭环供应链网络规划

在市场需求、设施开设成本和产品回收率不确定的条件下,采用一种交互式可能性规划方法,研究由多个工厂、分销点、市场和废旧点构成的可持续闭环供应链网络设计问题。基于可持续闭环供应链网络结构,构建以企业运营成本和环境伤害最小、社会效益最大为目标的混合整数规划模型。同时,引入改进Epsilon约束方法将多目标优化问题转化为单目标优化问题,在此基础上提出一种两阶段可能性规划方法,基于TH模糊方法对不确定性参数进行处理。最后,通过数值实例,验证本文所建可持续闭环供应链网络模型的有效性,并对悲观-乐观值、不确定参数最低可接受水平β、可调参数γ进行敏感性分析;通过与其他模糊方法对比表明,采用TH模糊方法能得到稳定的最优解。     

Multi-objective geometric programming problem with ∊-constraint method

In multi-objective geometric programming problem there are more than one objective functions. There is no single optimal solution which simultaneously optimizes all the objective functions. Under these conditions the decision makers always search for the most “preferred” solution, in contrast to the optimal solution. A few mathematical programming methods namely fuzzy programming, goal programming and weighting methods have been applied in the recent past to find the compromise solution. In this paper ?$?$-constraint method has been applied to find the non-inferior solution. A brief solution procedure of ?$?$-constraint method has been presented to find the non-inferior solution of the multi-objective programming problems. Further, the multi-objective programming problems is solved by the fuzzy programming technique to find the optimal compromise solution. Finally, two numerical examples are solved by both the methods and compared with their obtained solutions.     

模糊机会约束规划下的投资组合模型

资产的过去数据和专家对资产未来表现的判断是资产收益率的两个重要信息,本文用基于上述两个信息的可能性分布描述证券收益率的不确定性,结合可能性测度和必要性测度,建立了基于模糊机会约束规划的乐观型和悲观型投资组合模型,并且得到了各模型的最优解的解析式。最后给出了算例予以说明。     

Fractional Goal Programming for Fuzzy Solid Transportation Problem with Interval Cost

In this paper, we study a solid transportation problem with interval cost using fractional goal programming approach (FGP). In real life applications of the FGP problem with multiple objectives, it is difficult for the decision-maker(s) to determine the goal value of each objective precisely as the goal values are imprecise, vague, or uncertain. Therefore, a fuzzy goal programming model is developed for this purpose. The proposed model presents an application of fuzzy goal programming to the solid transportation problem. Also, we use a special type of non-linear (hyperbolic) membership functions to solve multi-objective transportation problem. It gives an optimal compromise solution. The proposed model is illustrated by using an example.     

Some Difficulties in Applying Interactive Compromise Programming Illustrated by a Recent Method and a Case Study

The paper presents some of the difficulties in applying interactive, and interactive compromise, programming methods to real-life problems. These include the problem of dominated solutions when multiple optimal solutions exist to single objective function optimization, cycling, and the depiction of the analyst as a silent spectator in the decision making process. By taking as an example a recently proposed interactive compromise programming method, these difficulties are illustrated and necessary modifications are proposed to overcome these difficulties. These are further elucidated by a case study.     

The optimization technique for solving a class of non-differentiable programming based on neural network method

In this paper, the optimization techniques for solving a class of non-differentiable optimization problems are investigated. The non-differentiable programming is transformed into an equivalent or approximating differentiable programming. Based on Karush–Kuhn–Tucker optimality conditions and projection method, a neural network model is constructed. The proposed neural network is proved to be globally stable in the sense of Lyapunov and can obtain an exact or approximating optimal solution of the original optimization problem. An example shows the effectiveness of the proposed optimization techniques.     

Fuzzy goal programming for multiobjective transportation problems

Several fuzzy approaches can be considered for solving multiobjective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.     

电视广告效果预测模型

本文通过引入电视广告影响力指数 ,利用主成分分析和多维偏好线性规划方法 ,结合广告理论 ,构建了电视广告效果的预测模型 ;对模型的基本原理和求解方法作了分析和讨论 ,并通过一真实的广告案例对模型进行了检验 .     

Two-level value function approach to non-smooth optimistic and pessimistic bilevel programs

ABSTRACT

The authors' paper in Dempe et al. [Necessary optimality conditions in pessimistic bilevel programming. Optimization. 2014;63:505–533], was the first one to provide detailed optimality conditions for pessimistic bilevel optimization. The results there were based on the concept of the two-level optimal value function introduced and analysed in Dempe et al. [Sensitivity analysis for two-level value functions with applications to bilevel programming. SIAM J. Optim. 22 (2012), 1309–1343], for the case of optimistic bilevel programs. One of the basic assumptions in both of these papers is that the functions involved in the problems are at least continuously differentiable. Motivated by the fact that many real-world applications of optimization involve functions that are non-differentiable at some points of their domain, the main goal of the current paper is to extend the two-level value function approach by deriving new necessary optimality conditions for both optimistic and pessimistic versions in bilevel programming with non-smooth data.     

An intuitionistic fuzzy programming method for group decision making with interval-valued fuzzy preference relations

The paper develops a new intuitionistic fuzzy (IF) programming method to solve group decision making (GDM) problems with interval-valued fuzzy preference relations (IVFPRs). An IF programming problem is formulated to derive the priority weights of alternatives in the context of additive consistent IVFPR. In this problem, the additive consistent conditions are viewed as the IF constraints. Considering decision makers’ (DMs’) risk attitudes, three approaches, including the optimistic, pessimistic and neutral approaches, are proposed to solve the constructed IF programming problem. Subsequently, a new consensus index is defined to measure the similarity between DMs according to their individual IVFPRs. Thereby, DMs’ weights are objectively determined using the consensus index. Combining DMs’ weights with the IF program, a corresponding IF programming method is proposed for GDM with IVFPRs. An example of E-Commerce platform selection is analyzed to illustrate the feasibility and effectiveness of the proposed method. Finally, the IF programming method is further extended to the multiplicative consistent IVFPR.