基于模糊推理的贴近度决策方法及应用

利用模糊综合评判和模糊推理相结合,建立不同评价空间之间的映射,提出了基于模糊推理的贴近度决策方法,通过实例对比分析,验证基于贴近度的优选决策的结果也是合理的。     

基于可能性理论的案例决策方法研究

提出基于可能性理论与基于案仍推理相结合的双层CBDT决策方法。第1层：决策者应用基于案例推理方法,通过建立在连续集合上问题的可能性分布,结合不同的决策方法对决策问题的不确定性进行定性分析;第2层：利用期望效用理论在与问题的可能性分布相关联的方案中选择具体方案。该方法可避免以往基于案例决策方法中所蕴含的一些技术难题。     

Multiple criteria decision making and decision support systems — Guest editor's introduction

Integration of MCDM with DSS brings benefit to both fields. MCDM tools are useful in identifying and evaluating incompatible alternatives for DSS, while DSS can implement MCDM approaches and help maintain and retrieve MCDM models. Over the years, MCDM has made considerable contribution to the development of various DSS subspecialties. This special issue on Multiple Criteria Decision Making and Decision Support Systems consists of 9 selected papers from the 20th International Conference on Multiple Criteria Decision Making. The guest editors highlight the key ideas and contributions of the papers in the special issue.     

Why pay attention to paths in the practice of environmental modelling?

Taking the ‘path perspective’ helps to understand and improve the practice of environmental modelling and decision making. A path is the sequence of steps taken in a modelling project. The problem solving team faces several forks where alternative choices can be made. These choices determine the path, together with the impact of uncertainties and exogenous effects. This paper discusses phenomena that influence the problem solvers' choices at the forks. Situations are described where it can be desirable to re-direct the path or backtrack on it. Phenomena are identified that can cause the modelling project to get stuck on a poor path. The concept of a path draws attention to the interplay of behavioral phenomena and the sequential nature of modelling processes. This helps understand the overall effect of the behavioral phenomena. A path checklist is developed to help practitioners detect forks and reflect on the path of the modelling project.     

Information support systems for problem solving

The occurrence in the literature of success stories dealing with (management) information systems is rare indeed. This paper addresses some of the possible factors that make it difficult to design and implement successful information (and decision support) systems. More specifically, we consider the role of problem formulation and specification, including goal setting, in the design of information systems.     

Four-layer framework for combinatorial optimization problems domain

Four-layer framework for combinatorial optimization problems/models domain is suggested for applied problems structuring and solving: (1) basic combinatorial models and multicriteria decision making problems (e.g., clustering, knapsack problem, multiple choice problem, multicriteria ranking, assignment/allocation); (2) composite models/procedures (e.g., multicriteria combinatorial problems, morphological clique problem); (3) basic (standard) solving frameworks, e.g.: (i) Hierarchical Morphological Multicriteria Design (HMMD) (ranking, combinatorial synthesis based on morphological clique problem), (ii) multi-stage design (two-level HMMD), (iii) special multi-stage composite framework (clustering, assignment/location, multiple choice problem); and (4) domain-oriented solving frameworks, e.g.: (a) design of modular software, (b) design of test inputs for multi-function system testing, (c) combinatorial planning of medical treatment, (d) design and improvement of communication network topology, (e) multi-stage framework for information retrieval, (f) combinatorial evolution and forecasting of software, devices. The multi-layer approach covers ‘decision cycle’, i.e., problem statement, models, algorithms/procedures, solving schemes, decisions, decision analysis and improvement.     

Fuzzy methods for case-based recommendation and decision support

The paper proposes two case-based methods for recommending decisions to users on the basis of information stored in a database. In both approaches, fuzzy sets and related (approximate) reasoning techniques are used for modeling user preferences and decision principles in a flexible manner. The first approach, case-based decision making, can principally be seen as a case-based counterpart to classical decision principles well-known from statistical decision theory. The second approach, called case-based elicitation, combines aspects from flexible querying of databases and case-based prediction. Roughly, imagine a user who aims at choosing an optimal alternative among a given set of options. The preferences with respect to these alternatives are formalized in terms of flexible constraints, the expression of which refers to cases stored in a database. As both types of decision support might provide useful tools for recommender systems, we also place the methods in a broader context and discuss the role of fuzzy set theory in some related fields.     

The strengths and limitations of teams for detecting problems

Problem detection in operational settings requires expertise and vigilance. It is a difficult task for individuals. If a problem is not detected early enough, the opportunity to avoid or reduce its consequences may be lost. Teams have many strengths that individuals lack. The team can attend to a wider range of cues than any of the individuals can. They can offer a wider range of expertise, represent different perspectives, reorganize their efforts to adapt to situational demands, and work in parallel. These should improve problem detection. However, teams can also fall victim to a wide range of barriers that may reduce their alertness, mask early problem indicators, confound attempts to make sense of initial data, and restrict their range of actions. Therefore, teams may not necessarily be superior to individuals at problem detection. The capability of a team to detect problems may be a useful measure of the team’s maturity and competence.

Can DSS evolve without changing our view of the concept of ‘problem’?

DSS have almost exclusively been presented in the context of problem solving. But the term ‘problem’ is never defined. It is unfortunately a fairly ambiguous term whose meaning oscillates from the observation of an unsatisfactory, objective reality which must be corrected, to the subjective representation of one or more actors faced with a reality which he or they perceived as unsatisfactory. The first of these interpretations (i.e., a problem as an unsatisfactory objective reality) implicitly dominates the DSS literature and design methods, which does not avoid the hidden major stumbling block of ‘problem definition’. We believe that the adoption of the second interpretation (i.e., a problem as a subjective representation) is a better guarantee of the effectiveness of DSS and leads to different design methods (we propose one based on a systemic method oriented towards ‘soft problems’) and opens new horizons for potential application to DSS.     

Supporting decision making process with “ideal” software agents - What do business executives want?

According to Simon’s (1977) decision making theory, intelligence is the first and most important phase in the decision making process. With the escalation of information resources available to business executives, it is becoming imperative to explore the potential and challenges of using agent-based systems to support the intelligence phase of decision-making. This research examines UK executives’ perceptions of using agent-based support systems and the criteria for design and development of their “ideal” intelligent software agents. The study adopted an inductive approach using focus groups to generate a preliminary set of design criteria of “ideal” agents. It then followed a deductive approach using semi-structured interviews to validate and enhance the criteria. This qualitative research has generated unique insights into executives’ perceptions of the design and use of agent-based support systems. The systematic content analysis of qualitative data led to the proposal and validation of design criteria at three levels. The findings revealed the most desirable criteria for agent based support systems from the end users’ point view. The design criteria can be used not only to guide intelligent agent system design but also system evaluation.     

Applying Bayesian networks to performance forecast of innovation projects: A case study of transformational leadership influence in organizations oriented by projects

The focus of this work is the analysis of the influence of transformational leadership on organizational factors, and their impacts on the project performance. The factors considered are communication, flexibility, continuous delivery and continuous improvement, overlap of activities, and maturity of the team, in projects with a high degree of innovation. Bayesian networks were chosen as a simulation tool. Results showed that for a moderate level of overlap of activities, the maximum project performance is obtained when the leadership components individual consideration, inspirational motivation, idealized influence and intellectual stimulation, are either at moderate levels. This leads to high levels of team maturity, flexibility and continuous delivery, while continuous improvement and communication tend to be moderate. It is highlighted the characterization of the individual contribution of the variables to the project performance and the empirical application of Bayesian networks, as an alternative to statistical methods commonly employed in leadership and management studies.     

陆军合同作战指挥决策过程建模

现代作战的快速性,使得指挥员完成决策任务必须依赖于计算机辅助决策的帮助,而开发实用的决策支持系统需要有决策过程模型作为基础.针对目前这类过程模型存在的不足,根据陆军合同作战指挥的特点,通过将决策者和辅助决策者以及计算机应用程序功能综合纳入建模过程,提出了"一条主线、两层分级、三种关系,四种活动"为要素的主从式建模方法,其特点是便于决策者、辅助决策者以及参与决策者等相关人员的相互了解和沟通,突出了计算机应用程序在模型中应具有的基本功能,在辅助决策系统的分析设计上,为军事和技术人员提供了一种便于交互和便于理解的表示方式.     

Progression of mental models throughout the phases of a computer-based instructional simulation: Supportive information,practice, and performance

Students of chemical engineering (n = 26) participated in an experiment using a computer-based simulation of a chemical plant. The progression of participants’ mental models was examined throughout a computer-based instructional experience as they acquired the complex cognitive skills of troubleshooting. Participants’ mental models of the complex learning task were matched against an expert mental model at five observation points through the instruction. Progressions of learners’ mental models were examined before and after three phases of the instructional process: supportive information presentation, problem solving practice, and performance test. The results indicated a significant change in participants’ mental models after receiving the supportive information and little change after practice or performance. This paper presents the results of this investigation and discusses the findings and their implications for computer-based instruction and training.     

Integrated evidential reasoning approach in the presence of cardinal and ordinal preferences and its applications in software selection

A combination of cardinal and ordinal preferences in multiple-attribute decision making (MADM) demonstrates more reliability and flexibility compared with sole cardinal or ordinal preferences derived from a decision maker. This situation occurs particularly when the knowledge and experience of the decision maker, as well as the data regarding specific alternatives on certain attributes, are insufficient or incomplete. This paper proposes an integrated evidential reasoning (IER) approach to analyze uncertain MADM problems in the presence of cardinal and ordinal preferences. The decision maker provides complete or incomplete cardinal and ordinal preferences of each alternative on each attribute. Ordinal preferences are expressed as unknown distributed assessment vectors and integrated with cardinal preferences to form aggregated preferences of alternatives. Three optimization models considering cardinal and ordinal preferences are constructed to determine the minimum and maximum minimal satisfaction of alternatives, simultaneous maximum minimal satisfaction of alternatives, and simultaneous minimum minimal satisfaction of alternatives. The minimax regret rule, the maximax rule, and the maximin rule are employed respectively in the three models to generate three kinds of value functions of alternatives, which are aggregated to find solutions. The attribute weights in the three models can be precise or imprecise (i.e., characterized by six types of constraints). The IER approach is used to select the optimum software for product lifecycle management of a famous Chinese automobile manufacturing enterprise.     

RespiDiag: A Case-Based Reasoning System for the Diagnosis of Chronic Obstructive Pulmonary Disease

In this paper a decision support system for the diagnosis of a very serious respiratory disease caused by tobacco named the chronic obstructive pulmonary disease is presented. The system is based on case-based reasoning principles and gathers the experience of experts of the pneumology department of Dorban Hospital (Annaba, Algeria). A critical issue about the case base is that some values of the features are missing in most cases. Five approaches for managing this problem of missing data are proposed. Three of them allow evaluating the similarity despite the missing information. The two other approaches fill the voids with plausible values using a statistical method and the principles of case-based reasoning itself.     

Comparison of customer balking and reneging behavior to queueing theory predictions: An experimental study

In this paper, we study customer decision-making while in a queuing situation. Customers can either join a queue or balk and return at a later time. Customers who join can renege and also return later. Our objective is to determine whether people seem to follow the benchmarks provided by queuing theory or whether psychological costs and perceptions of time invalidate these benchmarks. We use a computerized experiment where participants face explicit financial rewards and penalties for their decisions in a between subjects, fully crossed design with two experimental factors—clock data, and information about expected waiting time, each at two levels, presence and absence. Evaluated against the queuing theory benchmark, decision-making is quite good. Reneging is very rare, as queuing theory requires. Most participants follow a consistent rule for balking. They balk at every line longer than some critical value, as prescribed by queueing theory. But, even when corrected for heterogeneity in time perception, this critical value is greater than the one that minimizes expected waiting time. The large critical value may be due to risk-aversion or participants overestimating the switching cost. The results are supported by a second experiment using different parameters. Information improved decisions for most participants by increasing the precision of waiting time estimates. In addition, information helps participants who underestimate waiting time to correctly leave the line and those who over-estimate to stay. Providing clock time had almost no impact on decision-making.     

An incident information management framework based on data integration, data mining, and multi-criteria decision making

An effective incident information management system needs to deal with several challenges. It must support heterogeneous distributed incident data, allow decision makers (DMs) to detect anomalies and extract useful knowledge, assist DMs in evaluating the risks and selecting an appropriate alternative during an incident, and provide differentiated services to satisfy the requirements of different incident management phases. To address these challenges, this paper proposes an incident information management framework that consists of three major components. The first component is a high-level data integration module in which heterogeneous data sources are integrated and presented in a uniform format. The second component is a data mining module that uses data mining methods to identify useful patterns and presents a process to provide differentiated services for pre-incident and post-incident information management. The third component is a multi-criteria decision-making (MCDM) module that utilizes MCDM methods to assess the current situation, find the satisfactory solutions, and take appropriate responses in a timely manner. To validate the proposed framework, this paper conducts a case study on agrometeorological disasters that occurred in China between 1997 and 2001. The case study demonstrates that the combination of data mining and MCDM methods can provide objective and comprehensive assessments of incident risks.     

基于直觉模糊推理的多属性群决策方法研究*

针对属性取值以直觉模糊数形式给出的多属性决策问题,提出了基于直觉模糊推理的多属性群决策方法。首先针对专家的评价信息构建直觉决策推理规则,然后根据规则之间的关系给出了决策推理模型,进而给出了基于直觉模糊推理的决策方法;最后通过购房实例验证了该方法的正确性和有效性。     

A cognitivist model for decision support: COGITA project, a problem formulation assistant

The formulation of a problem may be defined as a process of acquisition and organization of knowledge related to a given situation, on which a decision maker projects some action. The assistance in the problem formulation that we may expect within decision support systems is difficult to design and to implement. This is mainly due to the frequent lack of attention to a sufficiently formalized conceptual framework which would consider the decision with a more cognition sciences oriented approach. In the first part, we will present an instrumental model for the study of decision processes as an attempt to simulate the cognitive process of knowledge acquisition and organization carried out by a decision maker facing a problematic situation. Considering its epistemological foundations, this model can be named “cognitivist model”. Within this model, the decision is defined as a cognitive construction which we call “decisional construct”. It consists of the elaboration of one or several abstract representations of the problematic situation (formulation phase), and the design of operational models (solving phase). In the second part, we will present the COGITA project, which consists of the design and realization of an environment for the development of problem formulation assistance systems. The modelization and simulation of cognitive processes call for relevant techniques originating either in artificial intelligence or in connectionism. We will show which are the main characteristics, potentials, limits and complementarity of these techniques and why their integration is fundamental and necessary to the simulation of the cognitive process associated with the formulation. COGITA is a hybrid system currently under development which tends to integrate symbolic artificial intelligence techniques and connectionist models in a cooperative hybridation the general architecture of which is presented.