Comparative analysis of UTA multicriteria methods

This paper reviews the main variants of the utility additive (UTA) multicriteria method, and systematically compares their predictive performance on two sets of data. It analyses both those cases where the model provides a ranking with errors and those without errors. First, it shows that the reference projects should be chosen carefully in order to elicit as much information as possible from the decision maker: a set of projects satisfying a fractional factorial plan is recommended. Second, it discusses the use of alternative post-optimality methods for solving the problem of multiple solutions and their different predictive performances. Third, it presents the results of simulations based on utility functions involving interdependence between criteria, and shows that UTA handles this problem effectively by an adjustment of its coefficients. Finally, the influence of the model's parameters on the predictive performance is also investigated.     

Global maximization of UTA functions in multi-objective optimization

The UTAs (UTilité Additives) type methods for constructing nondecreasing additive utility functions were first proposed by Jacquet-Lagrèze and Siskos in 1982 for handling decision problems of multicriteria ranking. In this article, by UTA functions, we mean functions which are constructed by the UTA type methods. Our purpose is to propose an algorithm for globally maximizing UTA functions of a class of linear/convex multiple objective programming problems. The algorithm is established based on a branch and bound scheme, in which the branching procedure is performed by a so-called I-rectangular bisection in the objective (outcome) space, and the bounding procedure by some convex or linear programs. Preliminary computational experiments show that this algorithm can work well for the case where the number of objective functions in the multiple objective optimization problem under consideration is much smaller than the number of variables.     

基于UTA方法的关键基础设施系统的保护研究

关键基础设施系统是城市的生命线,但常因突发事件频发而具有不稳定性和脆弱性。一个好的保护方案应该综合考虑关键基础设施系统的韧性和成本等多个目标,但这些目标常常相互冲突而使决策陷入困境。本研究从多目标优化角度入手,探索用UTA方法确定保护关键基础设施系统最佳方案的可行性,UTA法通过与决策者互动获取决策者的偏好信息,并基于线性规划和价值函数原理构建了决策者偏好模型。通过C县电网系统的案例分析发现UTA方法可帮助决策者在决策过程中考虑多目标之间的冲突与权衡,从而确定最满意的决策方案。     

Numerical discovery and experimental confirmation of vortex ring generation by microramp vortex generator

An implicitly implemented large eddy simulation (ILES), by using the modified fifth order WENO scheme, is applied to study the flow around the microramp vortex generator (MVG) at Mach 2.5 and Re_θ = 5760. A series of new discoveries on the flow around supersonic MVG have been made by the UTA LES team including source of the momentum deficit, inflection points (surface in 3-D), Kelvin–Helmholtz instability and vortex ring generation. Most of the new discoveries, which were made by the UTA LES team and presented in 2009, were confirmed by experiment conducted by the UTA experiment team in 2010. A new 5-pair-vortex-tube model near the MVG is given based on the ILES observation.     

ACUTA: A novel method for eliciting additive value functions on the basis of holistic preference statements

In multiple criteria decision aiding, it is common to use methods that are capable of automatically extracting a decision or evaluation model from partial information provided by the decision maker about a preference structure. In general, there is more than one possible model, leading to an indetermination which is dealt with sometimes arbitrarily in existing methods. This paper aims at filling this theoretical gap: we present a novel method, based on the computation of the analytic center of a polyhedron, for the selection of additive value functions that are compatible with holistic assessments of preferences. We demonstrate the most important characteristics of this technique with an experimental and comparative study of several existing methods belonging to the UTA family.     

UTA-poly and UTA-splines: Additive value functions with polynomial marginals

Additive utility function models are widely used in multiple criteria decision analysis. In such models, a numerical value is associated to each alternative involved in the decision problem. It is computed by aggregating the scores of the alternative on the different criteria of the decision problem. The score of an alternative is determined by a marginal value function that evolves monotonically as a function of the performance of the alternative on this criterion. Determining the shape of the marginals is not easy for a decision maker. It is easier for him/her to make statements such as “alternative a is preferred to b”. In order to help the decision maker, UTA disaggregation procedures use linear programming to approximate the marginals by piecewise linear functions based only on such statements. In this paper, we propose to infer polynomials and splines instead of piecewise linear functions for the marginals. In this aim, we use semidefinite programming instead of linear programming. We illustrate this new elicitation method and present some experimental results.     

Using artificial intelligence and visual techniques into preference disaggregation analysis: The MUDAS system

Multicriteria Disaggregation-Aggregation (D-A) approaches include two major issues: The first one is to assess the decision maker's preference model from his global preferences on a reference set of alternative actions. The second issue concerns the interactive character of the approach in order to aid the DM to understand his/her own way of preferring. This paper aims to present the Multicriteria Interactive Intelligence Decision Aiding System (MIIDAS), an implementation of the extended UTA II method. Also, the contribution of the Artificial Intelligence and Visual techniques, embedded into the procedures of the MIIDAS system, is presented. Finally, a case study coming from a portfolio management selection problem is used to illustrate the system.     

A Personnel Selection Problem in Healthcare System Using Fuzzy-TOPSIS Approach

The methods of multiple criteria decision-making (MCDM) are increasingly becoming the most desired tools for making daily decisions in various fields of human endeavors. Staff employment in any sector requires a thorough evaluation of the applicant before selection to ensure effective and efficient service delivery. Besides, healthcare is one of the most complicated organizations dealing with human lives. This paper has developed a staff selection model considering a fuzzy environment by using the technique for order preference similar to the ideal solution (TOPSIS) method. For the delivery and promotion of quality healthcare systems, medical staff selection is crucial to the system. Therefore, the study evaluates medical staff by using the expert''s linguistic judgement under the criteria of skill, experience and ability to respond to a problem. The expert''s vagueness in judgments has been represented by using fuzzy triangular numbers. The study determines the closeness coefficient, the measures of separation and the ideal solutions of the TOPSIS method. The most appropriate medical staff are ranked and selected based on the available criteria. The Fuzzy-TOPSIS method is simple and can help other organizations achieve proper ranking, evaluation and selection of qualified candidates, as it takes imprecise information into account.     

基于数值分布的激励型综合评价方法

针对激励评价中的等级划分问题,本文提出了一种基于数值分布的等级划分方法,相比于现有的等级划分方法,该方法能够综合考虑数值分布情况来划分等级,并结合本文提出的等级划分法对密度算子进行拓展,提出了一种基于数值分布的激励型综合评价方法。首先本文从数值分布的角度提出了一种新的等级划分方法,从而得出各等级区间的等级区间分界点;其次确定等级系数,并结合指标值和等级区间分界点给出各指标的权向量,给出一种不需要进行归一化处理的等级权向量确定方法,该方法能够较好的解决归一化处理带来的不公平性;再次根据密度算子思想对评价数据进行集结得出评价结果;最后通过一个算例对该方法进行验证,结果表明该方法可以实现对被评价对象科学激励的作用。该方法尤其适用于企业员工激励、省市综合排名、高校人才选拔等问题。     

高校人力资源配置优化模型的研究

通过对高校人力定岗、现有人力考核、人才引进考核三个方面进行阐述,给出了构建高校人力资源指标体系的原则、方法和流程.运用数理统计和AHP方法(层次分析法)建立了高校教师定岗模型和现有人员考核模型,对高校人事管理部门对未来的师资建设提供了参考依据。     

Performance appraisal based on distance function methods

Performance appraisal is a process used by some firms to evaluate their employees’ efficiency and productivity in order to plan their promotion policy, salary policy, layoffs policy, etc. Initially this process was just carried out by the executive staff, but recently it has evolved into an evaluation process based on the opinion of different reviewers, supervisors, collaborators, customers and the employees themselves (360-degree method). In such an evaluation process the reviewers evaluate some indicators related to employees performance appraisal. In this paper we propose an evaluation framework where there are different sets of reviewers taking part in the evaluation process. Since reviewers have a different knowledge about the evaluated employee, it seems suitable to offer a flexible framework in which different reviewers can express their assessments in different finite scales according to their knowledge. The final aim is to compute a global evaluation for each employee, that can be used by the management team to make their decisions regarding their human resources policy. In this way, to obtain a global evaluation for each employee, we propose a methodology able to aggregate individual valuation in a metric L_p framework. In this context, the associated optimization problems can be reduced to an Extended Goal Programming formulation that is very easy to compute.     

Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method

We present a method called Generalized Regression with Intensities of Preference (GRIP) for ranking a finite set of actions evaluated on multiple criteria. GRIP builds a set of additive value functions compatible with preference information composed of a partial preorder and required intensities of preference on a subset of actions, called reference actions. It constructs not only the preference relation in the considered set of actions, but it also gives information about intensities of preference for pairs of actions from this set for a given decision maker (DM). Distinguishing necessary and possible consequences of preference information on the considered set of actions, GRIP answers questions of robustness analysis. The proposed methodology can be seen as an extension of the UTA method based on ordinal regression. GRIP can also be compared to the AHP method, which requires pairwise comparison of all actions and criteria, and yields a priority ranking of actions. As for the preference information being used, GRIP can be compared, moreover, to the MACBETH method which also takes into account a preference order of actions and intensity of preference for pairs of actions. The preference information used in GRIP does not need, however, to be complete: the DM is asked to provide comparisons of only those pairs of reference actions on particular criteria for which his/her judgment is sufficiently certain. This is an important advantage comparing to methods which, instead, require comparison of all possible pairs of actions on all the considered criteria. Moreover, GRIP works with a set of general additive value functions compatible with the preference information, while other methods use a single and less general value function, such as the weighted-sum.     

组织内部员工信息安全胜任评价模型

针对内部员工信息安全胜任评价这一重要问题,在分析内部信息攻击行为的特点及全面总结胜任特征相关研究成果的基础上,构建了内部员工信息安全胜任特征评价指标体系,在此基础上提出了组织内部员工信息安全胜任特征评价模型。模型基于安全风险防范思想,将评价环节分为两个环节,综合考虑了决策者制定决策偏好和候选员工的个性优势,更实际地反映了候选员工间的差别。最后通过案例分析验证了该评价模型的有效性和实用性。     

Evaluation of staffing policies in Markov manpower systems and their extension to organizations with outsource personnel

This paper takes up the problem of the evaluation of manpower policies followed in an organization, and examines it in relation to the career growth facilitation afforded by the system to its members. The method proposes and constructs a quantitative measure of effectiveness for manpower policies based on the career growth facilitation afforded to the members, which can then be used for comparative evaluation of different policies. The models are then extended to organizations which outsource work through outsource manpower, and which seek to control the blend of internal and outsource manpower. The models and analyses developed herein will be of relevance to all manpower systems, and in particular, to organizations which outsource work, and those that attempt to achieve desired blends of the outsource staff with their own.     

基于模糊数学和灰色理论的多层次综合评价方法及其应用

针对多层次指标体系的综合评价问题,在模糊综合评判方法的基础上,结合灰色理论,提出了一种新的定量综合评价方法—多层次模糊灰关联聚类分析综合评价方法.应用该方法对油气钻井技术进行综合评价,取得了满意的结果.     

基于粒子群优化的竞优评析拓展方法及评价引导功效——以TOPSIS模式为例

现有的竞优评析研究主要以线性加权、数据包络分析等线性模型为基础,缺少普适于非线性效用函数的竞优评析研究,限制了竞优评析理论的适用范围与发展空间。本文以TOPSIS效用函数为例,采用PSO优化算法解决非线性个体优势特征识别问题,实现竞优评析理论的拓展。最后,以人力资源管理中的人员业绩评价问题为例给出具体算例,对比验证了所提拓展方法的正确性与有效性;同时,结合计算结果所具有的“极端价值主张”现象,从评价方法的“后效”引导作用的角度,提出了评价方法的“引导功效”概念及其评判标准,供选择评价方法时参考。     

基于属性不完全补偿性的图书馆人员招聘研究

从人员招聘中属性的不完全补偿性出发,借助定性定量相结合的方法获取了评估数据,并对定性指标进行了差异性分析,增强主观评价指标的准确性.通过指标之间归一化处理,获取具有公度性的数据,并以图书馆人员的招聘为例,构架了一般的人员招聘评价模型,获得了较为理想的结果.     

带有奖惩作用的密度算子及应用

针对择优或罚劣的评价问题,对密度算子进行拓展,提出了带有奖惩作用的密度集结算子。该算子在给定奖惩等级的基础上,按照密度算子的思想对评价信息进行集结。首先,按照实际需要确定奖惩等级,并分别从奖励和惩罚两个方面给出了等级权重的确定方法;在此基础上,依据指标的发展情况给出了指标权重的获取方法;然后,对密度加权向量进行了界定,据此得到最终带有奖惩作用的评价结果。最后,通过一个算例对带有奖惩作用密度算子的应用进行了说明。该方法尤其适用于员工的激励或惩罚、优秀人才的甄选问题等。     

基于相对熵赋权和灰色理论的高校教师创新能力综合评价

提高高校教师的创新能力,对于践行科学发展观、增强高校市场竞争力,乃至推动国民经济的进步都具有重要意义.以高校教师创新能力综合评价为出发点,通过构建高校教师创新能力评价指标体系,建立基于层次分析法和灰色关联分析法的多指标综合评估模型,并通过案例介绍了模型的具体应用.     

基于简化的区间数据主成分分析的高校教师绩效分析

绩效分析是高校教师绩效管理中的重要组成部分.基于简化的区间数据主成分分析方法,以2008 2009年A大学经济管理学院的教师绩效评估数据为例,考察了两年内各岗位级别教师的整体绩效和变化情况,以及教师个人绩效在所处岗位级别上的动态变化情况.基于绩效分析的结果,对绩效管理决策提出了相关建议.