Yong-Hong Sun

A goal programming approach to group decision making based on multiplicative preference relations and fuzzy preference relations

Abstract This paper proposes a goal programming approach to solve group decision-making (GDM) problems where the preference information on alternatives provided by decision makers is represented in two different formats, i.e. multiplicative preference relations and fuzzy preference relations. In order to narrow the gap between the collective opinion and each decision maker’s opinion, a linear goal programming model is constructed to integrate the two different formats of preference relations and to compute the collective ranking values of the alternatives. Thus, the ranking of alternatives or selection of the most desirable alternative(s) is obtained directly from the computed collective ranking values. A numerical example is also used to illustrate the applicability of the proposed approach.

Evaluating knowledge management capability of organizations: a fuzzy linguistic method

Knowledge management capability (KMC) is the source for organizations to gain the sustainable competitive advantage. KMC evaluation is a required work with strategic significance. However it still has not been addressed in the existing literatures. So the objective of this study is to investigate a fuzzy multiple attributes decision-making method (FMADM) for evaluating KMC. In this paper, a framework for evaluating KMC is presented, which includes two parts, one is an evaluation hierarchy with attributes, the other a judgment matrix model with two dimensions to identify the evaluation results of KMC. Then, a fuzzy linguistic approach is proposed to evaluate the KMC of organizations. The evaluation results of KMC obtained through the proposed approach are objective and unbiased due to two reasons. Firstly, the results are generated by a group of experts in the presence of motile attributes. Secondly, the fuzzy linguistic approach employed in this paper has more advantage to reduce distortion and losing of information than other fuzzy linguistic approaches. Through evaluation result of KMC, managers could judge the necessity to improve the KMC and determine which dimension of KMC is the most needed direction to improve. Additionally, an example is used to illustrate the availability of the proposed method.

A Group Decision Support Approach to Evaluate Experts for R&D Project Selection

In R&D project selection, experts (or external reviewers) always play a very important role because their opinions will have great influence on the outcome of the project selection. It is also undoubted that experts with high-expertise level will make useful and professional judgments on the projects to be selected. So, how to measure the expertise level of experts and select the most appropriate experts for project selection is a very significant issue. This paper presents a group decision support approach to evaluate experts for R&D project selection. Where the criteria and their attributes for evaluating experts are summarized mainly based on the experience with the National Natural Science Foundation of China (NSFC). A formal procedure that integrates both objective and subjective information on experts is also presented. It is mainly based on analytic hierarchy process (AHP), scoring method, and fuzzy linguistic processing. A group decision support system is designed and implemented for illustration of the proposed method.

A hybrid knowledge and model approach for reviewer assignment

In R&D project selection, experts (or external reviewers) always play a very important role because their opinions will have great influence on the outcome of the project selection. It is also undoubted that experts with high expertise level will make useful and professional judgments on the projects to be selected. So, how to assign the most appropriate experts to the relevant proposals is a very significant issue. This paper presents a hybrid knowledge and model approach which integrates mathematical decision models with knowledge rules, for the assignment of external reviewers to R&D project proposals. The approach can be applied to government funding agencies in China and other countries.

A decision support approach for assigning reviewers to proposals

Peer review plays an important role in research project selection at funding agencies. Quality of peer review greatly depends on the degree of matching between reviewers and assigned research proposals. This kind of matching is largely determined by the process of assigning proposals to reviewers. As the number of proposals submitted to funding agencies continues to grow, the traditional approaches of finding appropriate reviewers for each individual proposal fail to satisfy the practical needs. This paper proposes an alternative approach whose basic idea is grouping proposals first and then assigning appropriate reviewers for each proposal group. Based on the idea, a decision support approach is proposed to identify valid proposals and reviewers, classify proposals and reviewers according to their disciplines, partition proposals into groups and assign reviewers to proposal groups. A system has been developed based on the proposed approach to facilitate the decision making process of assigning proposals to reviewers.

A collaborative e-learning system based on multi-agent

Based on the analysis of the process of the collaborative e-learning system and the characteristics of Multi-agent technology, this paper brings forward a collaborative e-learning system framework founded on Multi-agent. Meanwhile, it provides with the key technology to realize e-learning and the arithmetic to search for the collaborator, carries out further analysis of the system’s operating mechanism, multi-agent’s logic structure, Agent capacity and time complexity. According to this framework, it is easy to realize the Web-based e-learning among several individuals and the knowledge’s intertransferring, which is good to improve the e-learning efficiency and lift the knowledge level of the whole internet organization.

A Hybrid Knowledge and Model Approach for Reviewer Assignment

In R&D project selection, experts (or external reviewers) always play a very important role because their opinions will have great influence on the outcome of the project selection. It is also undoubted that experts with high expertise level will make useful and professional judgments on the projects to be selected. So, how to assign the most appropriate experts to the relevant proposals is a very significant issue. This paper presents a hybrid knowledge and model approach which integrates mathematical decision models with knowledge rules, for the assignment of experts to review of R&D project proposals. The approach can be applied to government funding agencies in China and other countries

An intelligent decision support approach for reviewer assignment in R&D project selection

Abstract In the process of Research and Development (R&D) project selection, experts play an important role because their opinions are the foundation on which to judge the potential value of a project. How to assign the most appropriate experts to review project proposals might greatly affect the quality of project selection, which in turn could affect the return on investment of the funding organization. However, in many funding organizations, current approaches to assigning reviewers are still based on simply matching the discipline area of the reviewers with that of the proposal, which could result in poor quality of project selection and poor future financial return. Additionally, these approaches might make it difficult to balance resources and resolve conflicts of interests between reviewers and applicants. Therefore, to overcome these problems, there is an urgent need for a systematic approach to support and automate the reviewer assignment process. This research aims at proposing an intelligent decision support approach for reviewer assignment and developing an Assignment Decision Support System (ADSS). In this approach, heuristic knowledge of expert assignment and techniques of operations research are integrated. The approach uses decision models to determine the best solution of reviewer assignment that maximizes the total expertise level of the reviewers assigned to proposals. It also balances the distribution of proposals at different grades and solves conflicts of interests between reviewers and applicants. Its application in the National Natural Science Foundation of China (NSFC) and the computational results of its effectiveness and efficiency are also described.

Measuring and Identifying Knowledge Sharing Capability of Organizations

Knowledge sharing is the core of knowledge management in organizations, and how to measure and identify the knowledge sharing capability of organizations is an important issue in the knowledge sharing field. Firstly, the concept of knowledge sharing capability of organizations is proposed in this paper. Two dimensions, including technical and non-technical capability, which form the knowledge sharing capability of organizations, are analyzed. Then the measurement index system and method are given. Finally, a matrix model is established to identify the knowledge sharing capability of organizations and analyze the strength and causes of formation. At the same time, corresponding improvements are proposed.

Corrigendum: Corrigendum Evaluating knowledge management capability of organizations: A fuzzy linguistic method [Experts Systems with Applications 36 (2P2) (2009) 3346-3354]

Corrigendum Corrigendum ‘‘Evaluating knowledge management capability of organizations: A fuzzy linguistic method” [Experts Systems with Applications 36 (2P2) (2009) 3346–3354] Zhi-Ping Fan , Bo Feng *, Yong-Hong Sun , Wei Ou a a Department of Management Science and Engineering, School of Business Administration, Northeastern University, Shenyang 110004, China b Department of Information Systems, City University of Hong Kong, Kowloon, Hong Kong, China