不完全信息群体多属性决策的一种交互式方法

通过方案之间的两两比较,确定方案间的偏爱强度区间,借助于一致性检验和净偏爱强度,构造了一种交互式的群体多属性决策方法.该决策方法在经济规划、工业生产、交通运输、军事决策等诸多社会实际问题中均可有广泛的应用.     

An Interactive MOLP Procedure for the Extension of DEA to Effectiveness Analysis

This paper presents a new, interactive multi-objective linear-programming procedure to aid decision-makers in setting up goals for desired outputs. The procedure relies on empirical production functions generated by the use of data envelopment analysis. It presents the decision-maker with a set of alternative efficient points in order either to compare different sets of inputs in terms of their effectiveness for goal achievement, or to set goals against which future management performance may be measured. With each iteration the new information provided by the decision-maker is used to adjust the procedure, leading to points which have greater effectiveness utility for the decision-maker. A numerical example is provided along with guidelines for future applications.     

随机利率下信息非完全时的风险债券定价

本文在结构化模型的框架下,运用远期鞅方法推导了随机利率时不完全信息的风险债券的定价公式,并分析了公式里的五项重要指标对风险债券价格的影响.     

Conditional Value-at-Risk in Stochastic Programs with Mixed-Integer Recourse

In classical two-stage stochastic programming the expected value of the total costs is minimized. Recently, mean-risk models - studied in mathematical finance for several decades - have attracted attention in stochastic programming. We consider Conditional Value-at-Risk as risk measure in the framework of two-stage stochastic integer programming. The paper addresses structure, stability, and algorithms for this class of models. In particular, we study continuity properties of the objective function, both with respect to the first-stage decisions and the integrating probability measure. Further, we present an explicit mixed-integer linear programming formulation of the problem when the probability distribution is discrete and finite. Finally, a solution algorithm based on Lagrangean relaxation of nonanticipativity is proposed. Received: April, 2004     

Stochastic Programming with Recourse: A Modification from an Applications Viewpoint

The paper describes a modified "wait-and-see" approach to solving two-stage stochastic programming problems. The approach, which involves a detailed sensitivity analysis in the classical sense, is described within the frameworks of decision theory and probabilistic programming. Although optimality in the mathematical sense cannot be guaranteed by using the approach, it is suggested that the managerial benefits weigh heavily in its favour. The approach allows management to consider a wide variety of objectives in making the choice between alternatives and facilities detection of the cause of any infeasibility due to management policy constraints. In addition, it allows much simpler programming calculations and provides an upper bound on the benefits that can be obtained by solving the full "here-and-now" problem and thus a judgement of the worth of the added computational burden can easily be made.     

On a Problem of Optimal Stochastic Control with Incomplete Information

We study a problem of Bayesian adaptive control arising in mathematical finance. For this particular problem, we obtain explicit formulas for the value function and the optimal control policy.     

一种基于随机占优的多种信息形式的MADM方法

针对具有多种信息形式的多属性决策(MADM)问题,提出了一种决策分析方法。在本文中,首先描述了属性值为随机变量、清晰数和区间数三种信息形式的MADM问题;然后将这三种信息形式转化为带有累积分布函数的随机型信息的形式,并依据随机占优准则判断并确定两两方案之间比较的随机占优关系,在此基础上运用ELECTRE III级别高于关系方法得到方案的排序结果。最后,通过一个算例说明了本文给出方法的可行性和有效性。     

Solving Stochastic Linear Programs with Restricted Recourse Using Interior Point Methods

In this paper we present a specialized matrix factorization procedure for computing the dual step in a primal-dual path-following interior point algorithm for solving two-stage stochastic linear programs with restricted recourse. The algorithm, based on the Birge-Qi factorization technique, takes advantage of both the dual block-angular structure of the constraint matrix and of the special structure of the second-stage matrices involved in the model. Extensive computational experiments on a set of test problems have been conducted in order to evaluate the performance of the developed code. The results are very promising, showing that the code is competitive with state-of-the-art optimizers.     

An Interactive Approach for Project Selection

This paper describes an interactive method for presenting a sequence of feasible sets of indivisible projects to a decision-maker. For each set as a whole, the decision-maker evaluates its utilities with respect to each of several attributes; the utilities are then combined to give a single utility for the set.A sequence of zero-one programmes is used to ensure that the only sets presented are those which are feasible and which are not contained in larger feasible sets. The sets are presented in descending order of size, and the presentation can be terminated by the decision-maker or by supplementary rules.The method for presenting sets can be used in other contexts in which complete enumeration of possible sets would otherwise be required.     

Quantitative Stability Analysis of Two-Stage Stochastic Linear Programs with Full Random Recourse

Abstract

In this paper, we apply the parametric linear programing technique and pseudo metrics to study the quantitative stability of the two-stage stochastic linear programing problem with full random recourse. Under the simultaneous perturbation of the cost vector, coefficient matrix, and right-hand side vector, we first establish the locally Lipschitz continuity of the optimal value function and the boundedness of optimal solutions of parametric linear programs. On the basis of these results, we deduce the locally Lipschitz continuity and the upper bound estimation of the objective function of the two-stage stochastic linear programing problem with full random recourse. Then by adopting different pseudo metrics, we obtain the quantitative stability results of two-stage stochastic linear programs with full random recourse which improve the current results under the partial randomness in the second stage problem. Finally, we apply these stability results to the empirical approximation of the two-stage stochastic programing model, and the rate of convergence is presented.     

Convergent Cutting-Plane and Partial-Sampling Algorithm for Multistage Stochastic Linear Programs with Recourse

We propose an algorithm for multistage stochastic linear programs with recourse where random quantities in different stages are independent. The algorithm approximates successively expected recourse functions by building up valid cutting planes to support these functions from below. In each iteration, for the expected recourse function in each stage, one cutting plane is generated using the dual extreme points of the next-stage problem that have been found so far. We prove that the algorithm is convergent with probability one.     

An Isometric Approach to Generalized Stochastic Integrals

The possibility to extend the classical Ito's construction of stochastic integrals is studied. This construction can be applied to fractional Brownian motions with Hurst index H(0, 1/2). A change of variables formula for fractional Brownian motions in terms of the stochastic integrals is given.     

不完备区间值信息系统中基于相容-相似关系的模糊粗糙集方法

区间值信息系统是单值信息系统的推广模型.区间值是描述影响观测对象值的一种表示方法.然而,在实际应用中,由于遗漏、测量误差、数据传输、介质噪声等多种因素的影响,区间值往往会出现缺失值.如果忽略或丢弃包含缺失值的数据,则必须丢弃大量信息.主要针对不完备区间值信息系统,提出一种基于相容-相似关系的模糊粗糙集方法.在此基础上,推广了模糊近似算子的定义,研究了不确定性度量问题.     

An Interactive Stochastic Simulation Model of a Health Service District

This paper describes the role and structure of a model of a U.K. health district and its extensive use in the U.K. as a training aid in developing corporate planning skills.     

An Interactive Multiple Criteria Approach to Ranking Alternatives

In this paper we are concerned with ranking various orderings of a set of alternatives to a composite order as a multiple criteria problem. The orderings (called preference orderings) can be real preference orderings or any natural orderings. The objective is to find the most preferred order of the decision maker using the preference orderings as criteria.In principle, the problem can be formulated as a multiple objective linear programming problem using the model of Bowman and Colantoni and then solved with the interactive method proposed by Zionts and Wallenius. However, the fact that we are dealing with integer variables prohibits us from applying this approach as such. We discuss the problem formulation and propose a modified approach to that of Zionts and Wallenius for solving the problem.     

An Interactive Heuristic Approach for Multi-Objective Integer-Programming Problems

An interactive approach to solve the multi-objective integer-programming problem heuristically is described. The approach consists of two main parts. The first is an algorithm to guide the search for a set of weights to the objective functions which would produce the solution most preferred by the decision-maker given a linear utility function. The search area is successively decreased through an interaction process, with the decision-maker using a selection and contraction method. During each stage of this algorithm, a number of single integer-programming problems are solved heuristically. The motivation for this approach, along with some computational experimentation, is provided.     

An Interactive,Graphical Approach to Linear Financial Models

Early attempts to utilize linear algebraic (L.P.) techniques in financial planning met with little success. This was probably due to a misunderstanding of the process by which financial and corporate plans were formulated. The planning process is now better understood and the use of computer-based models is well accepted. However current modelling systems perform only simple arithmetic evaluations. There is a prima facie case for re-examining linear algebraic methods to exploit the considerable amount of information which is available within a plan. This paper describes the work done in the South of Scotland Electricity Board to develop an interactive financial modelling system.     

一种交互式遗传算法生成带主题乐曲的方法

在算法作曲领域中,遗传算法由于其不依赖于问题参数本身及具有较好的全局最优解等特性,被很多人应用于乐曲创作中,但是生成的乐曲也具有无主题和盲目性等缺陷.为解决这一问题,在适应度函数中定义了几条规则使得乐曲向有利于生成主题的方向进化,再通过人工评价使所选出的乐曲更能符合人们的欣赏习惯.实验结果表明,使用基于规则的交互式遗传算法所生成的带主题的乐曲能够满足人们的欣赏要求.