A Multi-Attribute Assignment Goal-Programming Model with Incentives

A multi-attribute assignment goal-programming model is developed in this paper for the selection and assignment of transfer personnel. Attributes and incentives are used to select the correct type of people from surplus personnel and assign them to vacant positions. The model is illustrated in a simple, exemplary case problem, and the results are interpreted. The model is solved by using a sequential linear goal-programming algorithm and a mixed-integer programming subroutine.     

A Linear Model for Estimating Project Resource Levels and Target Completion Times

Selecting target completion time and appropriate resource availabilities for projects is not easy. In this paper a simple relationship is established to aid the planner in this task. The relationship is based on experiments involving 4942 different network/resource configurations. The relationship can be used to select the best balance between the cost of over-runs and the cost of resources.     

Linear Model Selection Based on Risk Estimation

The problem of selecting one model from a family of linear models to describe a normally distributed observed data vector is considered. The notion of the model of given dimension nearest to the observation vector is introduced and methods of estimating the risk associated with such a nearest model are discussed. This leads to new model selection criteria one of which, called the "partial bootstrap", seems particularly promising. The methods are illustrated by specializing to the problem of estimating the non-zero components of a parameter vector on which noisy observations are available.     

A Network-Flow Algorithm for Project Selection

A simple network-flow algorithm is described for solving a class of problems, with various applications, including that of project selection. The algorithm should have wide appeal since no prior knowledge of network algorithms is required, and the familiar activity-on-node type of network provides the starting point for the algorithm. This work is an extension of an approach recently proposed by H. P. Williams.     

纵向数据下线性EV模型的变量选择

本文考虑了纵向数据线性EV模型的变量选择.基于二次推断函数方法和压缩方法的思想提出了一种新的偏差校正的变量选择方法.在选择适当的调整参数下,我们证明了所得到的估计量的相合性和渐近正态性.最后通过模拟研究验证了所提出的变量选择方法的有限样本性质.     

Multi-Objective and Goal-Programming Approaches as a Distance Function Model

This note shows that virtually all the multi-objective and goal-programming approaches can be thought of as special cases of a general distance-function model. Although the outcome of this paper cannot be considered completely new, it can, however, help increasing conceptual clarity and precision in future dialogue, showing the actual links between multi-objective programming, goal programming and traditional mathematical programming.     

A Reply to “Comments on an Algorithm for Solving the Linear Goal-Programming Problem by Solving its Dual”

In a recent paper, it was claimed that an algorithm published earlier by Ignizio (for the solution to the lexicographic linear goal-programming model) is in error. In this reply, it is noted that the alleged error simply does not exist but is due solely to the misapplication of the algorithm.     

Comments on “An Algorithm for Solving the Linear Goal-Programming Problem by Solving Its Dual”

Goal Programming within a pre-emptive priority structure has been one of the most widely used multi-objective mathematical model formulations. By considering its dual problem, Ignizio has shown that a very efficient computational procedure for solving linear goal-programming problems can be problems can be implemented with any conventional large-scale simplex system. This note gives an overview of Ignizio's dual formulation, and identifies when a certain dual variable must be incorporated in the model to preserve the pre-emptive priority structure.     

A Study of a Decision Model for R & D Project Selection

This paper reports a study made during 1966 within the Bristol Works of the British Aircraft Corporation, Guided Weapons Division into the evaluation and selection of company-funded R & D projects. After identifying the objectives and selection criteria used by the decision-makers involved in the selection process an attempt was made to establish the relative importance of and interactions between the various factors, with a view to deriving a model based on a project scoring system using a weighted sum of factor scores. However, analysis of the results obtained showed that each decision-maker had a different perception of the relevance and importance of the various factors to the objectives, so that insufficient data were obtained to achieve a fully representative model. It was shown that for such a model to usefully represent the real situation the interdependence of the selection criteria and objectives must be explicitly established.     

线性回归模型中变量选择方法综述

变量选择是统计分析与推断中的重要内容,也是当今研究的热点课题。本文系统介绍了线性回归模型中变量选择的研究概况和最新进展,并指出了有待进一步研究的问题。     

蠓的线性判别

本文在没有任何假设的条件下，建立了一个对蠓分类的数学模型．所用理论工具、计算工具都很简单，且经反馈检验，对已知种类的蠓模型判断完全正确．     

A Zero-One Goal-Programming Algorithm Using Partitioning and Constraint Aggregation

This paper offers a new approach to the solution of zero-one goal-programming problems. The number of non-zero variables required to satisfy a constraint completely is used to identify the priority levels that can be completely achieved, and also determine redundant constraints and invariant decision-variables. The priority levels that are so identified form the basis for a subprogramme of completely achievable constraints. These constraints are aggregated to form a single constraint, and a set of necessary and sufficient conditions are developed to generate the optimal solution. The algorithm has been coded in Pascal and compared with the Lee and Morris algorithm. The virtual CPU time required to solve those problems tested was less than 10 per cent of that required by the Lee and Morris algorithm. The algorithm may also be useful in solving other types of zero-one formulations.     

A Linear Risk Model

A special structure optimization model is presented which includes many of the single variable risk problems that are encountered in operational problems. A risk function is assumed which is a piece-wise linear function of some random variable whose distribution is known; one seeks the value of the decision variable which minimizes expected risk. In this paper are presented the necessary and sufficient conditions for this optimization for random variables which are either continuously or discretely distributed. The important special case of a continuous risk function is discussed; multiple risk problems with a joint constraint are analyzed; and the change in policy for a small change in the distribution of the random variable is investigated. Examples illustrate the application of the model.     

动态主动打断项目组合选择问题的模型研究

动态主动打断项目组合选择问题是项目组合选择研究的新方向,然而该问题中涉及的六个重要参数都是不确定的,需要通过预测或估算才能得到,导致项目组合选择结果存在风险,最优解随着参数的变动发生变化。针对这种情况,本文首先提出了一个适合敏感性分析的动态主动打断项目组合选择新模型,并将其线性化;其次,运用GAMS＼BARON求解算例,对比动态和静态主动打断项目组合选择模型的结果,验证了动态线性模型的优越性;最后,分别求解六个不确定性参数的敏感性系数,并进行了局部敏感性分析。结果表明:第一,动态主动打断项目组合选择线性模型既可以统筹安排新、旧项目,又能增加企业收益;第二,根据系数敏感性的排名,企业应当有区别地对待六个不确定性参数;第三,新项目投资和项目收益的敏感性大小和排序,会随着参数变动而变化。     

A PDAE Model for Interconnected Linear RLC Networks

In electrical circuit simulation, a refined generalized network approach is used to describe secondary and parasitic effects of interconnected networks. Restricting our investigations to linear RLC circuits, this ansatz yields linear initial-boundary value problems of mixed partial-differential and differential-algebraic equations, so-called PDAE systems. If the network fulfils some topological conditions, this system is well-posed and has perturbation index 1 only: the solution of a slightly perturbed system does not depend on derivatives of the perturbations. As method-of-lines applications are often used to embed PDAE models into time-domain network analysis packages, it is reasonable to demand that the analytical properties of the approximate DAE system obtained after semidiscretization are consistent with the original PDAE system. Especially, both should show the same sensitivity with respect to initial and boundary data. We will learn, however, that semidiscretization may act like a deregularization of an index-1 PDAE model, if an inappropriate type of semidiscretization is used.     

多属性决策中的目标规划

针对只有部分权重信息的对方案有偏好的多属性决策问题,本文给出了一种简单的目标规划模型,通过对该模型的求解却可得到决策方案的排序。最后给出了一个算例。     

A Mixed-Integer Goal-Programming Formulation of the Standard Flow-Shop Scheduling Problem

Until recently, the majority of models used to find an optimal sequence for the standard flow-shop problem were based on a single objective, typically makespan. In many applications, the practitioner may also want to consider other criteria simultaneously, such as mean flow-time or throughput time. As makespan and flow-time are equivalent criteria for optimizing machine idle-time and job idle-time, respectively, these additional criteria could be inherently considered as well. The effect of job idle-time, measuring in-process inventory, could be of particular importance.This paper presents an extension of an earlier model developed by the authors, formulating the generalized N job, M machine standard flow-shop problem as a mixed-integer goal-programming model. The formulation was empirically tested through a comparison of the generated optimal solutions to their mathematically derived counterparts. For this, a special flow-shop problem with certain permutation properties was selected. In addition, a randomly chosen problem was solved to show the general applicability of the derived formulation.The model allows the incorporation of the makespan as well as the mean flow-time criteria, instead of optimization being based on a single objective.     

离散线性投资组合模型的分枝定界算法

本文提出了一类新的带整数交易手数和凹型交易费用的均值绝对偏差模型(MAD)和极大极小投资组合模型(Minmax),并给出了离散模型的分枝定界算法.我们分别用随机产生的数据和Nasdaq股票市场的真实数据进行了数值实验,数值分析表明在一定的收益水平下均值绝对偏差离散模型风险控制上优于极大极小投资组合离散模型,而计算效率上极大极小投资组合离散模型优于期望绝对偏差离散模型.