Global Priority Estimation in Multiperson Decision Making

The analytic hierarchy process generalized to synthesizing local preferences across a complex hierarchy structure into global priorities in multiperson decision making is considered in various approaches. They include classic eigenvector synthesis and multiplicative method, and several other suggested techniques, such as three-dimensional eigenvectors, and simultaneous linear and nonlinear estimations by whole hierarchy structure in synthetic optimizing procedures. A numerical example from marketing research with many criteria, subcriteria, alternatives, and respondents is presented. The quality of approximation compared by different approaches shows that the best results are produced by the nonlinear synthetic priority techniques. The techniques described have been successfully used in dozens of real-world projects and are very helpful for practical managerial decision making in complex hierarchies. Dr. Stan Lipovetsky is Senior Research Director, GfK Custom Research North America, Marketing Science, Research Center for Excellence. The author thanks Professor T. Rapcsak and two reviewers for insightful suggestions that improved the paper.     

Optimal risk probability for first passage models in semi-Markov decision processes

This paper studies the risk minimization problem in semi-Markov decision processes with denumerable states. The criterion to be optimized is the risk probability (or risk function) that a first passage time to some target set doesn't exceed a threshold value. We first characterize such risk functions and the corresponding optimal value function, and prove that the optimal value function satisfies the optimality equation by using a successive approximation technique. Then, we present some properties of optimal policies, and further give conditions for the existence of optimal policies. In addition, a value iteration algorithm and a policy improvement method for obtaining respectively the optimal value function and optimal policies are developed. Finally, two examples are given to illustrate the value iteration procedure and essential characterization of the risk function.     

基于主客观权重集成的电子商务网站评价方法

研究了不确定决策环境下企业电子商务网站评价问题。利用专家给出的区间互补判断矩阵,获得属性的主观权重;定义了区间数的平均差,提出了一种属性客观权重确定方法——平均差极大化方法;通过加权系数把主观权重与客观权重线性集成,使得到的综合权重同时反映主观和客观程度,并给出了一种基于投影模型的决策方案排序方法。最后通过算例说明该方法的可行性和有效性。     

Qualitative operators for reasoning maps: Evaluating multi-criteria options with networks of reasons

Cognitive/causal maps have been widely used as a powerful way of capturing decision-makers’ views about a problem, representing it as a cause–effect discourse. Several ways of making causal inferences from this type of model have been proposed in the Operational Research and Artificial Intelligence literatures, but none, as far as we are aware, has attempted to use a causal map structure to perform a multi-criteria evaluation of decision alternatives. Recently, we have proposed a new multi-criteria method, denominated as a Reasoning Map, which permits the use of decision-makers’ reasoning, structured as a network of means-and-ends (a particular type of causal map) to perform such an evaluation. In this manner, the model resembles the way that people talk and think about decisions in practice. The method also pays explicit attention to the cognitive limitations of decision-makers in providing preference information. Thus it employs qualitative assessment of preferences, utilises aggregation operators for qualitative data and provides also qualitative outputs. In this paper we discuss and evaluate possible ways of aggregating qualitative performance information in Reasoning Maps.     

A multi-parametric programming approach for multilevel hierarchical and decentralised optimisation problems

In this paper, we outline the foundations of a general global optimisation strategy for the solution of multilevel hierarchical and general decentralised multilevel problems, based on our recent developments on multi-parametric programming and control theory. The core idea is to recast each optimisation subproblem, present in the hierarchy, as a multi-parametric programming problem, with parameters being the optimisation variables belonging to the remaining subproblems. This then transforms the multilevel problem into single-level linear/convex optimisation problems. For decentralised systems, where more than one optimisation problem is present at each level of the hierarchy, Nash equilibrium is considered. A three person dynamic optimisation problem is presented to illustrate the mathematical developments.     

A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data

Seafloor classification using acoustic remote sensing techniques is an attractive approach due to its high-coverage capabilities and limited costs. The multi-beam echo-sounder (MBES) system provides high-resolution bathymetry and backscatter information with 100% coverage. In this paper, we present a seafloor classification method that employs the MBES backscatter data. The method uses the averaged backscatter data per beam. It, therefore, is independent on the quality of the MBES calibration. Also, its performance is insensitive to seafloor type variation along the MBES swathe and corrections for the angular dependence of the backscatter are not needed. The method accounts for the ping-to-ping variability of the backscatter intensity. It estimates both the number of seafloor types present in the survey area and the probability density function for the backscatter strength at a certain angle for each of the seafloor types. Application of the method to MBES backscatter data acquired in a well-known test area in the North Sea shows very good agreement with available ground truth. The method’s discriminatory performance for this area is demonstrated to be comparable to that of taking samples of the sediment. All seafloor types known to be present in the area are resolved for. Application of the method to the Stanton bank data set shows clearly separable areas that differ in seafloor composition.     

A Graded Quadrivalent Logic for Ordinal Preference Modelling: Loyola–Like Approach

We extend a quadrivalent logic of Belnap to graded truth values in order to handle graded relevance of positive and negative arguments provided in preferential information concerning ranking of a finite set of alternatives. This logic is used to design the preference modelling and exploitation phases of decision aiding with respect to the ranking problem. The graded arguments are presented on an ordinal scale and their aggregation leads to preference model in form of four graded outranking relations (true, false, unknown and contradictory). The exploitation procedure combines the min-scoring procedure with the leximin rule. Aggregation of positive and negative arguments as well as exploitation of the resulting outranking relations is concordant with an advice given by St. Ignatius of Loyola (1548) how to make a good choice.     

Analysis of multiobjective decision problems by the indifference band approach

A new approach, based on indifference band, for analyzing problems with multiple objectives is described. The relations of this approach to some previous results are given. Methods for generating nondominated solutions are supplied.     

A note on asymptotic inference in a class of non-stationary processes

In this note some problems of asymptotic inference in a class of non-stationary stochastic processes are considered. In particular, it is shown that no criterion based on the existence of uniformly most powerful tests over a local neighborhood can be used in this situation.     

瓶罐玻璃行业企业绩效评价

笔者依据所能获得的资料———《瓶罐玻璃行业企业经济信息汇编》[6],建立了一套经济实用的瓶罐玻璃行业企业绩效评价指标体系;提供了一种科学评价方法和计算机化的评价技术;能适时地对该行业内各企业绩效进行评价和比较分析;及时获得各企业在全行业内竞争位次;及时发现影响企业绩效的主要因素;为高层管理者制定和调整企业管理决策提供了参考依据。