群体多目标决策联合有效解类的几何特性

群体多目标决策是群体决策和多目标决策的一个交叉研究领域,借助供选方案的有效数,文[1]引进了群体多目标决策问题的联合有效解类概念,并且建立了这些解类的K－T最优条件,本文研究这类解的几何特性,得到若干基本的必要条件一充分条件。     

Decision making on stock levels in cases of uncertain demand rate

Inventory control is a typical problem of decision making. In this paper a periodic replenishment of stock, the spare parts being of one kind, is discussed for some cases when the demand rate is uncertain. The first decision, before all others in the sequence, is done by assuming an a priori distribution of demand rate. In time, as the demand process goes on, corrections of parameters of the a priori distribution are made according to the accumulated knowledge about past demand. This Bayesian approach to decision making based on learning about the uncertain demand rate is known for the case when the demand rate is unknown but constant. It is shown that this same approach can be used in some cases when the demand rate is unknown and not constant. Results are given and used for inventory control.     

Exploring the Role of Individual and Socially Constructed Knowledge Mobilization Tasks in Revealing Preservice Elementary Teachers' Understandings of a Triangle Fraction Task

This study compares the effectiveness of two forms of a knowledge mobilization task on preservice elementary teachers' (n= 65) performance in solving a triangle fraction problem. The study then identifies the source of the successful solutions by linking solutions to earlier activities. One group worked with the triangle fraction task individually; a second worked with the triangle fraction task in a social constructivist setting; a control group had no knowledge mobilization pretask. Although there was no significant difference in the frequency of successful solutions among treatment groups, a chi‐square analysis found that the social‐constructivist pretask group applied fewer ideas from the manipulative lessons as solutions to the posttask than did the comparison groups. The social constructivist group was, however, most successful at generating novel solutions to the triangle problem. The potential benefits of individual and socially constructed knowledge mobilization tasks are discussed.     

Expert systems approach to decision support

An approach to building decision support systems based on expert systems methods is proposed. The relatively weak basic assumptions include only stationarity (repeatability) of the decision in the same environment (circumstances) and ordering of the values of all the attributes with regard to the decision maker's preferences. The proposed approach is aimed at reflecting the experienced domain expert's and decision maker's knowledge and preferences, both in the form of facts and rasoning rules. Among the purposes of the described class of decision support systems there are the storage and retrieval of the expert's knowledge and decisions, decision making support and ranking of admissible decision alternatives. A general model of the decision process is proposed and a language for representation of the expert's knowledge is introduced in brief. The structure, reasoning control, and an example of application of the proposed system are discussed and possible further extensions are pointed out.     

解决一类群决策问题的改进德尔菲法

专家综合评价属于委员会形式的群决策问题,提出了基于一致性分析的群决策机制和基于OWA算子的群决策机制,用来弥补常用机制的缺陷;介绍了判断群体意见分歧特征和定量判断标准;提出了基于一致性分析的改进德尔菲法,介绍了这种方法的思想、特点和应用步骤;分析了这类群决策的人员组成和工作程序图.     

Fuzzy Mathematical Programming: A Unified Approach Based On Fuzzy Relations

Fuzzy mathematical programming problems (FMP) form a subclass of decision - making problems where preferences between alternatives are described by means of objective function(s) defined on the set of alternatives. The formulation a FMP problem associated with the classical MP problem is presented. Then the concept of a feasible solution and optimal solution of FMP problem are defined. These concepts are based on generalized equality and inequality fuzzy relations. Among others we show that the class of all MP problems with (crisp) parameters can be naturally embedded into the class of FMP problems with fuzzy parameters. We also show that the feasible and optimal solutions being fuzzy sets are convex under some mild assumptions.     

A decision support for identifying crucial knowledge requiring capitalizing operation

Capitalizing on the company’s crucial knowledge is an important operation for company’s success. The first step in knowledge capitalizing is to identify the crucial knowledge for which capitalizing operation is required. The crucial knowledge identification is an ill-structured and multi-criteria problem. In this paper, the crucial knowledge identification problem is structured according to the well-known Simon’s decision making process. Then, a decision support system (DSS), called K-DSS, is developed to support this decision process. The main objectives of this paper are to introduce the crucial knowledge identification decision process, to present the conceptual and functional architectures of K-DSS and to illustrate its utility through a real-world case study conducted in an automobile company.     

Developing models of decision making

Studies that focus on the decision making process can be classified in (at least) two ways: by psychological content and by contextual content. The first describes the degree to which the analysis focuses on the mental processes associated with decision making and the second deals with the degree to which the analysis is dependent upon the study's context. Several studies are examined in terms of where they fall in this two-dimensional taxonomy. Two analyses of maritime command and control conducted by the authors are examined more fully within this taxonomy. Both are context full, but are at opposite ends of the psychological content spectrum. These categorizations are useful in focusing future research aimed at developing models of decision making.     

Decision Making and Optimization in Changeable Spaces, a New Paradigm

This paper proposes a new decision making/optimization paradigm, the decision making/optimization in changeable spaces (DM/OCS). The unique feature of DM/OCS is that it incorporates human psychology and its dynamics as part of the decision making process and allows the restructuring of the decision parameters. DM/OCS is based on Habitual Domain theory, the decision parameters, the concept of competence set, and the mental operators 7-8-9 principles of deep knowledge. The covering and discovering processes are formulated as DM/OCS problems. Some illustrative examples of challenging problems that cannot be solved by traditional decision making/optimization techniques are formulated as DM/OCS problems and solved. In addition, some directions of research related to innovation dynamics, management, artificial intelligence, artificial and e-economics, scientific discovery, and knowledge extraction are provided in the conclusion.     

On the incorporation of adversarial reasoning in course of action analysis and resource allocation

Course of Action analysis and Resource Management are concerned with the allocation of resources over time to effect desired actions as a result of the perceived situation awareness. Decision Support Systems provide automated recommended courses of action to decision makers, considering relevant resource capabilities and constraints. Incorporating potential adversary actions and reactions to the current course of action decision (and the resources effecting the actions) in the decision making process will make the decision support system more robust and increase confidence that the recommended decisions are appropriate responses to the unfolding situations. We discuss research results from the inclusion of possible adversary actions and reactions into the course of action/resource allocation decision making framework. The overall decision problem is formulated as a multi-stage mathematical program. As the problem is NP-hard, an heuristic is developed through a natural problem decomposition. Simulated results show the effectiveness of the heuristic in producing good-quality solutions in an efficient manner.     

Some two-echelon style-goods inventory models with asymmetric market information

A “two-echelon” newsboy problem considers the interactive decisions between the “manufacturer” and the “retailer” – now recognized as two separate entities. Earlier papers on this problem assumed that the two parties share the same market information. We extend this problem by studying the situation in which the retailer has better market information than the manufacturer. Presented are several decision models that should be useful for guiding a manufacturer's decisions. Moreover, solutions to these models lead to various unexpected and interesting conclusions. For example, we found that improved retailer's market-knowledge always benefits the manufacturer and the system, though not necessarily the retailer himself. In contrast, improved manufacturer's knowledge benefits only the manufacturer himself at the expense of the retailer and of the system.     

状态离散的确定性多阶段群体决策的满意策略

为了求解状态离散的确定性多阶段群体决策问题,建立了多阶段群体决策模型,定义了群体Pareto最优策略、群体满意策略等概念,依据Bellman最优性原理,提出了多阶段群体决策问题的逆向递推算法,并通过引入偏好关系,得到了各阶段的子过程群体满意策略以及全过程群体满意策略,最后给出了一个计算实例．     

大型群体决策问题中的简单多数规则（3个评选对象场合）

一个群体决策问题取决于两个因素,一个是群体决策的规则,另一个是投票。当选定群体决策规则时,一个群体决策问题由投票完全决定,此时,群体决策问题与投票之间一一对应。简单多数规则是个简单且被广泛采用的群体决策规则,但它有缺陷,我们可举出些群体决策问题使用简单多数规则没法从投票得到最后群体决策的结果。这里我们将给出一个简单多数规则的有趣性质,即在3个评选对象场合,使用简单多数规则没法从投票得到最后群体决策结果的n个评选人的群体决策问题的个数与所有n个评选人的群体决策问题的个数之比当评选人个数n趋向无穷时趋于零,这说明3个评选对象的大型群体决策场合,简单多数规则的缺陷不严重。     

基于距离测度及支持度的混合评价信息供应商选择决策

新零售模式的推进迫使企业不断加强供应链节点上的供应商选择优化,是企业新时代发展面临的新课题。针对供应商选择优化提出一种基于距离测度及支持度的群决策方法,根据不同类型属性评价信息的距离测度定义,得到不同决策者之间关于单一属性指标的相互支持度,从而确定单个供应商各属性的群体综合评价值,并采用灰关联法对备选供应商进行排序择优。     

Dimension reduction in multiobjective optimization

In multiobjective optimization there is often the problem of the existence of a large number of objectives. For more than two objectives there is a difficulty with the representation and visualization of the solutions in the objective space. Therefore, it is not clear for the decision maker the trade-off between the different alternative solutions. Thus, this creates enormous difficulties when choosing a solution from the Pareto-optimal set and constitutes a central question in the process of decision making. Based on statistical methods as Principle Component Analysis and Cluster Analysis, the problem of reduction of the number of objectives is addressed. Several test examples with different number of objectives have been studied in order to evaluate the process of decision making through these methods. Preliminary results indicate that this statistical approach can be a valuable tool on decision making in multiobjective optimization. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A naïve approach for solving MCDM problems: the GUESS method

We present a simple multiple criteria decision making solution technique called the GUESS method. This method has been used in MCDM experiments where different solution methods have been compared. The GUESS method is an interactive solution method designed to be used with continuous multiple criteria decision problems. It is based on a class of solution methods called reference point methods whereby the decision maker generates a sequence of solutions based on a sequence of guesses or aspiration vectors. In this paper we explain the basic concepts of the GUESS method and describe the algorithm of the solution method. An illustrative example is provided, along with a discussion of the method from a behavioural decision making perspective.     

A case-based fuzzy multicriteria decision support model for tropical cyclone forecasting

This paper proposes a multi-stage framework for intelligent decision support. The proposed framework integrates case-based reasoning and fuzzy multicriteria decision making techniques. It potentially leads to more accurate, flexible and efficient retrieval of alternatives that are most similar and most useful to the current decision situation. Additionally, the framework provides intelligent assistance in articulating domain expert's preferences through outranking relations. We illustrated the proposed approach in the context of tropical cyclone prediction. Ten years of historical observation data about tropical cyclones was represented within fuzzy multicriteria decision-making problem. We describe a prototype intelligent decision support system, which helps the forecaster in retrieving best-fitted solutions in terms of both usefulness and similarity to the current observed case.     

A multiattribute GDSS for aiding problem-solving

This study proposes a group decision support system (GDSS) with multiattribute to help solve problems in the real world. The problems are usually characterized as a multiattribute decision making (MADM) for selections, and shall be the responsibility of an expert group. On a regular basis, experts within that group will meet and conduct discussions on the web. After each individual make efforts of judgments, comparisons, and rankings, they shall determine, collectively as a group, the final rankings of all possible alternatives. Furthermore, aimed at insuring the decision quality of the collective decisions, an integrated procedure will be applied to make any modifications as necessary. Based on the geometric aspects of decision quality, the disparity of each individual member's preferences on attribute can be filtered out by the suggested bounded indicators. And then the outliers related to attributes' weights will be identified through a different set of consensus indicators, thus, further improving the decision quality while maintaining a quantitative level of consensus. Finally, using a car-selection problem herein, the proposed integrated procedure is implemented on a network-based PC system with web interfaces.     

知识共享/转移背景下的动态群体决策绩效研究

现有的动态群体决策实践经常存在决策速度慢、观点不收敛的问题。本研究融入知识共享/转移的因素,从决策动态交互的过程视角,探讨决策绩效(一致性及决策时间)的改善机理、影响因素,从而提高相应绩效指标。研究通过构建决策过程模型,证明了知识共享/转移考虑下一致性序列在交互过程中的收敛性。同时,通过定性模拟判断知识共享/转移对决策时间的影响,以最小化决策时间为优化目标,搜索知识共享/转移水平,给出了个体知识差异构成的多维空间下最优的行动方案。     

Decomposition of a complex fuzzy controller for the truck-and-trailer reverse parking problem

The use of fuzzy logic has, in the last twenty years, become standard practice in the field of control. The reason lies in the fuzzy logic’s ability to relatively quickly transfer uncertain experience and knowledge about the observed object’s behaviour into the process of decision making. Nevertheless, one of the biggest problems that arises when using a fuzzy approach is the large number of fuzzy rules that have to be processed in order to produce one decision (i.e. one control output). The number of rules in a fuzzy controller primarily originates from the number of input variables that are entering the decision process and one possible solution for decreasing it is to use the method of decomposition. Its main goal is to implement the equivalent control functionality with a hierarchy of simpler fuzzy controllers. Their main characteristic is a lower number of input variables, which as a consequence leads to a smaller number of fuzzy rules. In our paper we apply the decomposition approach to the classical complex control case of the Truck-and-Trailer (T&T) reverse parking control problem. In such cases the implementation of control using only one fuzzy controller is very complex and the existing solutions, in some details, even deviate from the classical fuzzy approach. Our solution is, on the other hand, based only on the uncertain knowledge about the behaviour of the T&T driver and the results achieved are even better than those achieved by using the existing solutions.