Public Expenditure Forecasting and Control — The Practical Use of Distributed Lag Models

There are several reasons why expenditure managers need to forecast the financial progress of programmes accurately. The paper discusses the use of distributed lag models to assist in this and the practical decisions required in the process of selecting, setting up and calibrating the models.     

数学化归法在计算机中的应用

本从程序语言课程教学强调思维过程出发，提出了数学化归思考方法。     

The Use of Models for Environmental Forecasting and Corporate Planning

This paper presents briefly, the forecasting and planning activities which have evolved over the years in Esso Petroleum Company. It describes three computer models developed in the Corporate Planning Department for these activities an econometric model for short-term environmental forecasting and economic analysis; a horizon year optimization model for long-range corporate objectives setting; and a multi-time period simulation model for financial forecasting and planning both in short and long term. The structure, use and limitations of each of the models are discussed.     

The Tracking Problem for Distributed Systems with Unbounded Control Action

The tracking problem for systems defined by integral equations,given in terms of semigroups, is studied where we impose conditionsthat allow unbounded control action to be considered. It isshown that the optimal control is a combination of open-loopcontrol and the feedback control associated with the solutionto the quadratic cost regulator problem.     

Numerical Schemes for Discontinuous Value Functions of Optimal Control

In this paper we explain that various (possibly discontinuous) value functions for optimal control problem under state-constraints can be approached by a sequence of value functions for suitable discretized systems. The key-point of this approach is the characterization of epigraphs of the value functions as suitable viability kernels. We provide new results for estimation of the convergence rate of numerical schemes and discuss conditions for the convergence of discrete optimal controls to the optimal control for the initial problem.     

固定设计下计算机模型L2校准的渐近性质

计算机仿真模型广泛应用于工业工程等领域,借助真实的物理观测对计算机模型的校准参数进行估计,可以提高计算机模型的仿真精度.已有文献指出,当物理试验的试验点独立同分布于均匀分布时,计算机模型校准参数的L2估计半参数有效.然而真实的物理试验点往往不满足独立同分布于均匀分布这一条件,文章证明了在固定的试验点下,较准参数L2估计的相合性和渐近正态性,并通过数值模拟验证了L2估计的收敛速度.     

The Use of Antithetic Variates in Computer Simulations

The method of antithetic variates is a well known technique for reducing the variability of estimators in computer simulation experiments. It is shown that in many, if not most simulations, the usually suggested way of applying the method suffers from two weaknesses. One is so serious that it can lead to estimators which are worse than if the technique is not used at all. Simple alternatives are suggested which do not suffer either weakness. A numerical example is given illustrating the advantages of these alternative procedures.     

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     

The Stochastic Control Model for Use Conversion of Land

In this paper, the authors investigate the optimal conversion rate at which land use is irreversibly converted from biodiversity conservation to agricultural production. This problem is formulated as a stochastic control model, then transformed into a HJB equation involving free boundary. Since the state equation has singularity, it is difficult to directly derive the boundary value condition for the HJB equation. They provide a new method to overcome the difficulty via constructing another auxi...     

The Advantages of Fuzzy Optimization Models in Practical Use

Classical mathematical programming models require well-defined coefficients and right hand sides. In order to avoid a non satisfying modeling usually a broad information gathering and processing is necessary. In case of real problems some model parameters can be only roughly estimated. While in case of classical models the vague data is replaced by "average data", fuzzy models offer the opportunity to model subjective imaginations of the decision maker as precisely as a decision maker will be able to describe it. Thus the risk of applying a wrong model of the reality and selecting solutions which do not reflect the real problem can be clearly reduced. The modeling of real problems by means of deterministic and stochastic models requires extensive information processing. On the other hand we know that an optimum solution is finally defined only by few restrictions. Especially in case of larger systems we notice afterwards that most of the information is useless. The dilemma of data processing is due to the fact that first we have to calculate the solution in order to define, whether the information must be well-defined or whether vague data may be sufficient. Based on multicriteria programming problems it should be demonstrated that the dilemma of data processing in case of real programming problems can be handled adequately by modeling them as fuzzy system combined with an interactive problem-solving. Describing the real problem by means of a fuzzy system first of all only the available information or such information which can be achieved easily will be considered. Then we try to develop an optimum solution. With reference to the cost-benefit relation further information can be gathered in order to describe the solution more precisely. Furthermore it should be pointed out that some interactive fuzzy solution algorithms, e.g. FULPAL provide the opportunity to solve mixed integer multicriteria programming models as well.     

Continuous-Review Tracking Policies for Dynamic Control of Stochastic Networks

This paper is concerned with dynamic control of stochastic processing networks. Specifically, it follows the so called heavy traffic approach, where a Brownian approximating model is formulated, an associated Brownian optimal control problem is solved, the solution of which is then used to define an implementable policy for the original system. A major challenge is the step of policy translation from the Brownian to the discrete network. This paper addresses this problem by defining a general and easily implementable family of continuous-review tracking policies. Each such policy has the following structure: at each point in time t, the controller observes the current vector of queue lengths q and chooses (i) a target position z(q) of where the system should be at some point in the near future, say at time t+l, and (ii) an allocation vector v(q) that describes how to split the server's processing capacity amongst job classes in order to steer the state from q to z(q). Implementation of such policies involves the enforcement of small safety stocks. In the context of the heavy traffic approach, the solution of the approximating Brownian control problem is used in selecting the target state z(q). The proposed tracking policy is shown to be asymptotically optimal in the heavy traffic limiting regime, where the Brownian model approximation becomes valid, for multiclass queueing networks that admit orthant Brownian optimal controls; this is a form of pathwise, or greedy, optimality. Several extensions are discussed.     

Advanced Optimal Feedback Control for Tracking of Real-Life Applications

Calculating the open–loop solution of an optimal control problem is just the first step to cope with the practical realization of real life applications. Feedback controllers, like the classical Linear Quadratic Regulator (LQR), are needed to compensate pertubations appearing in reality. Although these controllers have proven to be a powerful tool in many applications and to be robust enough to countervail most differences between simulation and practice, they are not optimal if disturbances in the system data occur. If these controllers are applied in a real process, the possibility of data disturbances force recomputing the feedback control law in real–time to preserve stability and optimality, at least approximately. For this purpose, variations of the classical closed–loop controller with the extention to a trackingtype controller are analysed by means of an industrial application of container cranes. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Flaw in the Use of Minimal Defining Sets for Secret Sharing Schemes

It is shown that in some cases it is possible to reconstruct a block design uniquely from incomplete knowledge of a minimal defining set for . This surprising result has implications for the use of minimal defining sets in secret sharing schemes.     

The Use of Specially Structured Models for Obtaining Bounds in the Quadratic Assignment Problem

In this paper we study a procedure for finding bounds for the quadratic assignment problem. This procedure may be used as a sub-routine in hybrid procedures for solving this problem. The approach is based upon a data decomposition method, linking the actual data to the data of a special class of assignment problems for which bounds are computationally tractable.     

A Method for the Computer Calculation of Edgeworth Expansions for Smooth Function Models

Abstract

In this article I describe, in detail, a method for the computer calculation of Edgeworth expansions for a smooth function model accurate in the O(n ^–1) term. For such models, these expansions are an important tool for the analysis of normalizing transformations, the correction of an approximately normally distributed quantity for skewness, and the comparison of bootstrap inference procedures. The method is straightforward and is efficient in a sense described in the article. The implementation of the method in general is clear from its implementation in the Mathematica program (available through StatLib) for the particular case of the studentized mean.     

一类不确定混沌系统的自适应跟踪控制

对一类不确定混沌系统 ,讨论了系统的自适应跟踪控制问题 .基于 Lyapunov函数方法 ,构造出了一类新的自适应控制器 .该控制器的构造简单 ,并能控制着混沌系统的状态全局渐近跟踪预先给定的任何有界轨线 .仿真实例验证了所得控制器的有效性 .     

Integration of AI and OR Techniques for Computer-Aided Algorithmic Design in the Vehicle Routing Domain

This paper shows how tools and techniques of artificial intelligence can be successfully integrated into a computer system working in the vehicle routing domain. The aim of this system, called ALTO, is to facilitate the development of routing algorithms for transportation vehicles. In this paper, we describe the general algorithmic framework and the rich interface provided by the system to the expert algorithm designer. We also introduce a methodology for acquiring useful knowledge in the domain, based on examples of successful and unsuccessful problem-solving strategies. With such knowledge, ALTO would then be capable of actively supporting the algorithm designer by suggesting good candidate algorithms for solving new problems.     

Fast Statistical Surrogates for Dynamical 3D Computer Models of Brain Tumors

Understanding how malignant brain tumors are formed and evolve has direct consequences on the development of efficient methods for their early detection and treatment. Adequate mathematical models for brain tumor growth and invasion can be helpful in clarifying some aspects of the mechanism responsible for the tumor. These mathematical models are typically implemented in computer models, which can be used for computer experimentation to study how changes in inputs, such as growth and diffusion parameters, affect the evolution of the virtual brain tumor. The computer model considered in this article is defined on a three-dimensional (3D) anatomically accurate digital representation of the human brain, which includes white and gray matter, and on a time interval of hundreds of days to realistically simulate the tumor development. Consequently, this computer model is very computationally intensive and only small-size computer experiments can be conducted, corresponding to a small sample of inputs. This article presents a computationally efficient multidimensional kriging method to predict the evolution of the virtual brain tumor at new inputs, conditioned on the virtual brain tumor data available from the small-size computer experiment. The analysis shows that this prediction can be more accurate than a computationally competing model.     

一类可修复计算机系统的最优控制问题

利用Banach空间的相关理论,讨论了一类可修复计算机系统稳态解的最优控制问题,并证明了最优控制元的存在性与唯一性.结果表明,计算机系统的稳态可用度在有限时间内总能到达其期望值.     

投资控制模型解的渐近性质

本文利用初值比较法和边值比较法，讨论了投资系统解的渐适性质，得到了投资控制模型的平衡解是全局渐近稳定的．