一种求解半向量二层规划问题的基于KKT背离度量方程的粒子群优化算法

下层多目标规划问题的Pareto最优解的精确性对于成功求解半向量二层规划问题具有决定性作用.本文基于多目标规划问题的KKT背离度量方程,设计了具有确定性终止准则的半向量二层规划问题的粒子群算法.最后,利用线性半向量二层规划算例和非线性半向量二层规划算例进行数值仿真,仿真结果表明,算法中的KKT背离度量方程能有效控制下层问题Pareto最优解的精度,从而确保问题最优解的真实有效性.     

三参数区间灰数的多属性灰关联决策方法

针对决策信息不确定的多属性决策问题,利用三参数区间灰数的概率分布特征及经典灰关联决策的优势,提出了基于三参数区间灰数的灰关联决策方法.首先定义了三参数区间灰数决策向量与理想最优方案和临界方案决策向量的区间关联系数,其次得到所有方案决策向量的区间综合关联度,由区间综合关联度最大化和灰熵最大化确定属性的权重,进而对方案进行择优排序,最后用算例说明决策模型的合理性和实用性.     

一个具有变化联盟结构的动态合作博弈模型

本文运用合作博弈的观点分析和解决在动态决策进程中出现的合作方式发生变化的问题.针对于在博弈树给定的有限个节点上随机改变联盟剖分的动态博弈,通过引入新的特征函数和最优准则,建立了动态最优解PGN向量,同时给出了求最优路径和最优解的算法.     

非负费用折扣半马氏决策过程

本文考虑可数状态非负费用的折扣半马氏决策过程.首先在给定半马氏决策核和策略下构造一个连续时间半马氏决策过程,然后用最小非负解方法证明值函数满足最优方程和存在ε-最优平稳策略,并进一步给出最优策略的存在性条件及其一些性质.最后,给出了值迭代算法和一个数值算例.     

层次分析法中基于SVDD群体信息提取方法

针对层次分析法中群决策问题提出一种基于支持向量域描述(SVDD)的集结方法.首先利用生成树的方法把判断矩阵进行一致性剖分;然后利用支持向量域描述的方法排除干扰信息,找出群体公共信息,引入群体相容性,最优解等概念,提出并证明了关于群体信息球的特性;根据最大特征值法把群体信息球中的向量合成为群决策最优解,即关于方案的排序向量.并通过一个具体示例给出该方法的算法步骤同时显示了该方法的可行性和有效性.     

离散时间马氏决策过程的首达目标准则

本文考虑可数状态离散时间马氏决策过程的首达目标模型的风险概率准则.优化的准则是最小化系统首次到达目标状态集的时间不超过某阈值的风险概率.首先建立最优方程并且证明最优值函数和最优方程的解对应,然后讨论了最优策略的一些性质,并进一步给出了最优平稳策略存在的条件,最后用一个例子说明我们的结果.     

具有平均费用的非平稳Markov决策过程

本文研究了在一般状态空间具有平均费用的非平稳Ｍａｒｋｏｖ决策过程，把在平稳情形用补充的折扣模型的最优方程来建立平均费用的最优方程的结果，推广到非平稳的情形．利用这个结果证明了最优策略的存在性．     

多层规划和多目标规划的讨论

对任意给定的正整数 (n1,n2 ) ,构造了上下层决策变量分别是n1和n2 维的两层线性规划 ,其最优解不是相应双目标规划的有效解 ,进而构造出以任意给定的线性无关的向量d1,d2 为价格向量的两层规划 ,其最优解不是有效解 .这些讨论对现实问题的合理建模提供了理论依据 .此外 ,给出多层规划最优解是有效解的一个充分条件及判断其无效的方法 .     

动态规划中一类泛函方程的存在定理

动态规划中,有这样一类泛函方程求解问题(见[1]、[2])。现叙述如下: 设X,Y是Banach空间,S(?)X是状态空间。D(?)Y是决策空间,用x、y分别表示状态向量和决策向量,又设R为实数域,T:S×D→S,g:S×D→R,G:S×D×R→R.决策过程的返回函数f:S→R满足下面的泛函方程: 问当g,G和T满足什么条件时,方程(1)有解。 Bbakata-Mitra用Browder不动点定理研究了方程(1)解的存在性,得到了下述存     

基于灰色前景关联的多属性决策模型

针对方案属性值为三参数区间灰数的多属性决策问题,提出了一种基于灰色前景关联的多属性决策方法.定义了三参数区间灰数距离测度和排序方法;定义了靶心和靶界点,由此提出了子因素与最优理想效果向量和临界效果向量的灰色相对关联系数;分别以靶心和靶界点为参考点确定了前景价值函数,由此构建了求解最优权向量的优化模型,再求方案的综合前景值对方案进行排序.通过实例说明了该方法的合理性和有效性.     

A new condition for the existence of optimal stationary policies in average cost Markov decision processes

Discrete time countable state Markov decision processes with finite decision sets and bounded costs are considered. Conditions are given under which an unbounded solution to the average cost optimality equation exists and yields an optimal stationary policy. A new form of the optimality equation is derived for the case in which every stationary policy gives rise to an ergodic Markov chain.     

Analysis for some properties of discrete time Markov decision processes

This paper investigates properties of the optimality equation and optimal policies in discrete time Markov decision processes with expected discounted total rewards under weak conditions that the model is well defined and the optimality equation is true. The optimal value function is characterized as a solution of the optimality equation and the structure of optimal policies is also given.     

Optimal control of piecewise deterministic markov process

The trajectories of piecewise deterministic Markov processes are solutions of an ordinary (vector)differential equation with possible random jumps between the different integral curves. Both continuous deterministic motion and the random jumps of the processes are controlled in order to minimize the expected value of a performance functional consisting of continuous, jump and terminal costs. A limiting form of the Hamilton-Jacobi-Bellman partial differential equation is shown to be a necessary and sufficient optimality condition. The existence of an optimal strategy is proved and acharacterization of the value function as supremum of smooth subsolutions is also given. The approach consists of imbedding the original control problem tightly in a convex mathematical programming problem on the space of measures and then solving the latter by dualit     

New optimality conditions for average-payoff continuous-time Markov games in Polish spaces

This paper concerns two-person zero-sum games for a class of average-payoff continuous-time Markov processes in Polish spaces.The underlying processes are determined by transition rates that are allowed to be unbounded,and the payoff function may have neither upper nor lower bounds.We use two optimality inequalities to replace the so-called optimality equation in the previous literature.Under more general conditions,these optimality inequalities yield the existence of the value of the game and of a pair of ...     

Continuous time shock markov decision processes with discounted criterion

This paper presents a new concept of Markov decision processes: continuous time shock Markov decision processes, which model Markovian controlled systems sequentially shocked by its environment. Between two adjacent shocks, the system can be modeled by continuous time Markov decision processes. But according to each shock, the system's parameters are changed and an instantaneous state transition occurs. After presenting the model, we prove that the optimality equation, which consists of countable equations, has a unique solution in some function space Ω     

Discounted continuous-time Markov decision processes with unbounded rates and randomized history-dependent policies: the dynamic programming approach

This paper deals with a continuous-time Markov decision process in Borel state and action spaces and with unbounded transition rates. Under history-dependent policies, the controlled process may not be Markov. The main contribution is that for such non-Markov processes we establish the Dynkin formula, which plays important roles in establishing optimality results for continuous-time Markov decision processes. We further illustrate this by showing, for a discounted continuous-time Markov decision process, the existence of a deterministic stationary optimal policy (out of the class of history-dependent policies) and characterizing the value function through the Bellman equation.     

On the optimality equation for average cost Markov control processes with Feller transition probabilities

We consider Markov control processes with Borel state space and Feller transition probabilities, satisfying some generalized geometric ergodicity conditions. We provide a new theorem on the existence of a solution to the average cost optimality equation.     

Necessary and sufficient optimality conditions for control of piecewise deterministic markov processes

Piecewise deterministic Markov processes (PDPs) are continuous time homogeneous Markov processes whose trajectories are solutions of ordinary differential equations with random jumps between the different integral curves. Both continuous deterministic motion and the random jumps of the processes are controlled in order to minimize the expected value of a performance criterion involving discounted running and boundary costs. Under fairly general assumptions, we will show that there exists an optimal control, that the value function is Lipschitz continuous and that a generalized Bellman-Hamilton-Jacobi (BHJ) equation involving the Clarke generalized gradient is a necessary and sufficient optimality condition for the problem.     

Variance minimization for continuous-time Markov decision processes: two approaches

This paper studies the limit average variance criterion for continuous-time Markov decision processes in Polish spaces. Based on two approaches, this paper proves not only the existence of solutions to the variance minimization optimality equation and the existence of a variance minimal policy that is canonical, but also the existence of solutions to the two variance minimization optimality inequalities and the existence of a variance minimal policy which may not be canonical. An example is given to illustrate all of our conditions.