模糊多属性决策的三种结构元方法

将结构元理论引入到模糊多属性决策中,按照经典多属性决策的乐观型准则、悲观型准则和乐观-悲观结合型准则,对应地建立了基于模糊结构元理论的模糊乐观型、模糊悲观型、模糊乐观-悲观结合型决策方法。借助一个实例,本文运用这三种算法进行了决策,得出了和传统决策算法一致的结论。本文提出的算法不仅易于理解,而且计算的速度也远比传统算法要快,对于进一步研究模糊多属性决策问题有很好的参考作用。     

二层决策问题的灵敏度分析(2)

二层决策系统包含着两个最优化决策问题,其中上层决策问题的目标值是由下层决策的解所隐含地确定的.本文研究了二层决策问题的另一方面的灵敏度分析问题,讨论了上层决策者的价值系数发生变化而二层决策问题的最优解不变所产生的灵敏度分析问题.为了确定二层决策问题价值系数发生变化的范围,首先我们给出了灵敏度分析的基本方法,结合“k th-best”算法我们又给出了灵敏度分析的操作步骤.在所确定的变化范围内,价值系数的变化,不会引起二层决策问题的全局最优解的变化,从而为决策者提供了相对稳定的决策方案.最后我们给出了数值实例,它表明本文所给出的灵敏度分析的方法是正确的.     

Decision maker preferences for information in complex decision making: New directions for OR intervention

Operations research models are used in many business and non-business entities to support a variety of decision making activities, primarily well-defined, operational decisions. This is due to the traditional emphasis of these models on optimal solutions to pre-specified problems. Some attempts have been made to use OR models in support of more complex, strategic decision making. Traditionally, these models have been developed without explicit consideration for the information processing abilities and limitations of the decision makers, who interact with, provide input to, and receive output from such models.Research in judgement and decision making show that human decisions are influenced by a number of factors including, but not limited to, information presentation modes; information content, modes, e.g., quantitative versus qualitative; order effects such as primacy, recency; and simultaneous versus sequential presentation of data.This article presents empirical research findings involving executive business decision makers and their preferences for information in decision making scenarios. These preference functions were evaluated using OR techniques. The results indicate that decision makers view information in different ways. Some decision makers prefer qualitative, narrative, social information, whereas other prefer quantitative, numerical, firm specific information. Results also show that decision making tasks influence the preference structure of decision makers, but that in general, the preference are relatively stable across tasks.The results imply that for OR models to be more useful in support of non-routine decision making, attention needs to be focused on the information content and presentation effects of model inputs and outputs.     

一种新型区间数多属性决策算法研究

随着多属性决策问题的复杂性、不确定性日益增强，基于区间数的多属性决策理论与应用研究越来越广泛。基于算子有序性的构建思想，本文提出并构建了基于有序欧几里德算子的区间数多属性决策算法模型，证明了有序欧几里德算子的单调性、齐次性和幂等性。采用基于有序欧几里德算子的区间数多属性决策方法对某火炮采购问题进行了决策计算，证明了该方法的有效性与可行性。     

Approaches to multistage one-shot decision making

In this research, multistage one-shot decision making under uncertainty is studied. In such a decision problem, a decision maker has one and only one chance to make a decision at each stage with possibilistic information. Based on the one-shot decision theory, approaches to multistage one-shot decision making are proposed. In the proposed approach, a decision maker chooses one state amongst all the states according to his/her attitude about satisfaction and possibility at each stage. The payoff at each stage is associated with the focus points at the succeeding stages. Based on the selected states (focus points), the sequence of optimal decisions is determined by dynamic programming. The proposed method is a fundamental alternative for multistage decision making under uncertainty because it is scenario-based instead of lottery-based as in the other existing methods. The one-shot optimal stopping problem is analyzed where a decision maker has only one chance to determine stopping or continuing at each stage. The theoretical results have been obtained.     

食用豆腐生产经营过程中区间决策问题的研究

研究了豆腐作坊生产经营豆腐的最优决策问题.深入的实际调研表明:随着自然环境的改变、生产条件和市场资源的变化,豆腐作坊的净收益表现出不确定性.于是豆腐作坊的最优净收益就呈现出区间的特性.以豆腐作坊最优净收益为目标,首先建立了两大区间决策模型-改变生产规模的区间决策模型和不改变生产规模的区间决策模型.其次给出了有关区间决策的基本定理及求解方法.最后对实际问题进行了求解,从而为豆腐作坊提供了最优净收益的范围—区间,并对求得的结果进行了分析,说明所给的求解方法及所得结论是正确的.     

Combining compound linguistic ordinal scale and cognitive pairwise comparison in the rectified fuzzy TOPSIS method for group decision making

Group decision making is the process to explore the best choice among the screened alternatives under predefined criteria with corresponding weights from assessment of a group of decision makers. The Fuzzy TOPSIS taking an evaluated fuzzy decision matrix as input is a popular tool to analyze the ideal alternative. This research, however, finds that the classical fuzzy TOPSIS produces a misleading result due to some inappropriate definitions, and proposes the rectified fuzzy TOPSIS addressing two technical problems. As the decision accuracy also depends on the evaluation quality of the fuzzy decision matrix comprising rating scores and weights, this research applies compound linguistic ordinal scale as the fuzzy rating scale for expert judgments, and cognitive pairwise comparison for determining the fuzzy weights. The numerical case of a robot selection problem demonstrates the hybrid approach leading to the much reliable result for decision making, comparing with the conventional fuzzy Analytic Hierarchy Process and TOPSIS.     

Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

     

Effects of proactive decision making on life satisfaction

Proactive decision making, a concept recently introduced to behavioral operational research and decision analysis, addresses effective decision making during its phase of generating alternatives. It is measured on a scale comprising six dimensions grouped into two categories: proactive personality traits and proactive cognitive skills. Personality traits are grounded on theoretical constructs such as proactive attitude and proactive behavior; cognitive skills reflect value-focused thinking and decision quality. These traits and skills have been used to explain decision satisfaction, although their antecedents and other consequences have not yet been the subject of rigorous hypotheses and testing.This paper embeds proactive decision making within a model of three possible consequences. We consider—and empirically test—decision satisfaction, general self-efficacy, and life satisfaction by conducting three studies with 1300 participants. We then apply structural equation modeling to show that proactive decision making helps to account for life satisfaction, an explanation mediated by general self-efficacy and decision satisfaction. Thus proactive decision making fosters greater belief in one's abilities and increases satisfaction with one's decisions and with life more generally. These results imply that it is worthwhile to help individuals enhance their decision-making proactivity.Demonstrating the positive effects of proactive decision making at the individual level underscores how important the phase of generating alternatives is, and it also highlights the merit of employing “decision quality” principles and being proactive during that phase. Hence the findings presented here confirm the relevance of OR, and of decision-analytic principles, to the lives of ordinary people.     

多粒度语言信息的交互式多属性群决策方法及应用

针对目前多阶段交互式决策的研究多是基于点值的缺陷,以及大多数研究忽视了从多个属性对方案进行评价等不足,本文探讨了一种基于多粒度语言信息的交互式多属性群决策方法。首先基于不确定语言变量提出了多粒度语言信息的转换函数,并在给出若干假设的前提下描述了交互式多属性群决策的过程,然后提出了交互影响因子来度量决策者的“话语权”,通过稳定性指标探讨交互终止条件;接着在不确定语言变量的加权算术平均(ULWA)算子和诱导多阶段交互加权算子(I-UOWA和I-UOWGA)的基础上对偏好信息进行集结,再提出优势可能度法对方案排序。最后给出了一个物流供应商选择的案例研究。     

自然灾害情境下的终端供电设施应急抢修决策规划

自然灾害情境造成部分决策信息模糊和应急决策者的消极情绪。考虑消极情绪在自然灾害应急决策的影响,构建了考虑情绪下的前景理论的价值函数。运用反函数原理,根据应急决策者对模糊信息的心理感知价值,构建了其实际值的估计函数,用于解决决策信息模糊下的终端供电设施应急抢修决策规划问题,提出了相应的决策模型和求解算法。采用自主开发的在线实验系统,完成了信息模糊下的电网应急抢修决策实验,验证了决策模型和消极情绪下基于决策者心理感知对模糊信息估值方法的有效性。     

Approach to group decision making based on determining the weights of experts by using projection method

The aim of this paper is to present a new approach for determining weights of experts in the group decision making problems. Group decision making has become a very active research field over the last decade. Especially, the investigation to determine weights of experts for group decision making has attracted great interests from researchers recently and some approaches have been developed. In this paper, the weights of experts are determined in the group decision environment via projection method. First of all, the average decision of all individual decisions is defined as the ideal decision. After that, the weight of expert is determined by the projection of individual decision on the ideal decision. By using the weights of experts, all individual decisions are aggregate into a collective decision. Then an ideal solution of alternatives of the collective decision, expressed by a vector, is determined. Further, the preference order of alternatives are ranked in accordance with the projections of alternatives on the ideal solution. Comparisons with an extended TOPSIS method are also made. Finally, an example is provided to illustrate the developed approach.     

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.     

模糊德尔菲层次分析法及其应用

将传统的层次分析法、模糊评价方法与群体决策的德尔菲方法有机地结合起来,给出了一种系统分析的新方法—模糊德尔菲层次分析法(FDAHP),该方法在群体的德尔菲调查的基础上,将群体对事物的两两判断进行模糊处理,并将群体整体判断的结果作为决策环境参数,进一步根据群体决策的乐观系数最终确定出群体对事物的综合权重.用一个简单例示说明了该方法的具体执行步骤及其可行性.最后对该方法的优缺点作了简要讨论,并提出了今后相关问题的可能研究课题.     

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.     

Research in decision theory: A personal perspective

This essay offers a personal view of developments in two main areas of decision theory — decision making under risk and uncertainty, and social choice theory — with emphasis on the past half century and on likely future directions. Remarks on helpful research practices are also included.     

Novel applications of bipolar fuzzy graphs to decision making problems

Zhang introduced the concept of bipolar fuzzy sets as a generalization of fuzzy sets. Bipolar fuzzy sets have shown advantages in solving decision making problems than fuzzy sets. In this research paper, we study several different types of domination, including equitable domination, k-domination and restrained domination in bipolar fuzzy graphs. We present novel applications of bipolar fuzzy graphs to decision making problems. We also present an algorithm for computing dominating number in our applications.     

Exploring new directions for research in problem structuring methods: on the role of cognitive style

Prior research has argued that cognitive style can have a significant impact on group decision making. In addition, several scholars have proposed that cognitive style can play a key role in the design and use of group decision support systems. However, cognitive style has not received a great deal of attention in the problem structuring methods (PSMs) community. This is surprising, given that PSMs are specifically developed to support a group in their decision making. The purpose of this paper is thus to examine the significance of cognitive style within PSMs. The paper identifies and explores the role of four different cognitive style functions in problem structuring interventions. This analysis is carried out by focusing on the different tasks embedded within a group process supported by PSMs. Implications for the research and practice of PSMs are then discussed.     

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.     

Developing a straightforward approach for group decision making based on determining weights of decision makers

Group decision making is an active area of research within multiple attribute decision making. This paper assumes that all the decision makers (DMs) are not equally qualified to contribute equitably to the decision process. The aim of this paper is to develop an approach to determine weights of DMs, in which the decision information on alternatives with respect to attributes, provided by each DM, is represented in the form of interval data. We define the average of all individual decisions as the positive ideal decision (PID), and the maximum separation from PID as the negative ideal decision, which are characterized by a matrix, respectively. The weight of each DM is determined according to the Euclidean distances between the individual decision and ideal decisions. By using the obtained weights of DMs, all individual decisions are aggregated into a collective decision. Then the alternatives is ranked based on the collective decision. Meanwhile, this paper also gives a humanized decision method by using an optimistic coefficient, which is used in adjusting the relative importance between profit and risk. Finally, we give an example to illustrate the developed approach.