An approach to the match between panels of experts and groups of demanders in fuzzy linguistic environments

Abstract

Purpose Consulting experts is an effective way to utilize tacit resource. The purpose of the paper is to optimize the matching between panels of experts and groups of demanders to improve the efficiency of tacit knowledge sharing. Design/methodology/approach Experts and demanders express preferences using linguistic terms. The estimate method based on trust is developed to get missing ratings. Weights of demanders are determined and knowledge needs are identified. Three kinds of satisfaction are measured based on grey relational analysis. To maximize satisfaction of experts and demanders and safeguard meetings of knowledge needs as well as the workload of experts, the optimization model is constructed and the solution is optimal matching results. Findings The presented approach not only optimizes the matching between demanders and experts but also sets up a panel of experts in case that knowledge needs exceed a single expert’s capacity. Research limitations/implications The approach expands research works of methods for tacit knowledge sharing. The continuous updating of matching results and the processing of the data with mixing formats need to be studied further. Practical implications The presented approach acts as a valuable reference for the development of knowledge management systems. It can be used in any scene that needs the match between experts and demanders. Originality/value The approach provides a new way of helping demanders to find appropriate experts. Both experts’ and demanders’ preferences are considered. A panel of experts is set up when needed. Expert resources are utilized more efficiently and knowledge needs are met more comprehensively.