APPLICATION OF NEW TECHNIQUES OF ARTIFICIAL INTELLIGENCE IN LOGISTICS: AN ONTOLOGY-DRIVEN CASE-BASED REASONING APPROACH

摘要

In most cases, the project management is dealing with the "intelligent" reuse of know-how from previous projects and its adaptation to a similar, new project. Until now, purely quantitative and "hard" project management techniques like the critical path method and the project evaluation and review technique have been dominant. With this main stream approach, only simply structured logistics projects can normally be managed. In this paper, we present an ontology-driven case-based reasoning system (SCM Project Recom-mender) that can measure the similarity between knowledge collections, which are written in natural language. The application is implemented by using the open source case-based reasoning development framework jCOLIBRI.