Abstract:Treatment planning is a crucial and complex task in the social services industry. There is an increasing need for knowledge‐based systems for supporting caseworkers in the decision‐making of treatment planning. This paper presents a hybrid case‐based reasoning approach for building a knowledge‐based treatment planning system for adolescent early intervention of mental healthcare. The hybrid case‐based reasoning approach combines aspects of case‐based reasoning, rule‐based reasoning and fuzzy theory. The knowledge base of case‐based reasoning is a case base of client records consisting of documented experience while that for rule‐based reasoning is a set of IF–THEN rules based on the experience of social service professionals. Fuzzy theory is adopted to deal with the uncertain nature of treatment planning. A prototype system has been implemented in a social services company and its performance is evaluated by a group of caseworkers. The results indicate that hybrid case‐based reasoning has an enhanced performance and the knowledge‐based treatment planning system enables caseworkers to construct more efficient treatment planning in less cost and less time.

Abstract: Logistics service providers are professionals who serve clients such as manufacturers, raw material suppliers, distributors, retailers and shippers within the supply chain. Traditionally, the formulation of logistics strategies to execute various logistics services has been based solely on experience. In this paper, an intelligent case-based logistics strategy system (CLSS) is designed to help logistics service providers to develop strategies under various customers' demands. The proposed system, which is suitable for use in warehouse operations in the Hong Kong/Pearl River Delta region, enhances the effectiveness of logistics strategies formulations by integrating case-based reasoning technology seamlessly with various data management software such as a data warehouse, on-line analytical processing and a multidimensional database management system. Through applying CLSS in Eastern Worldwide Company, the resource utilization is maximized while work efficiency is greatly enhanced. As a result, the overall logistics operations are smoothened significantly.

Abstract: This paper presents an agent‐oriented and knowledge‐based system (AOKBS) for strategic e‐procurement. The AOKBS has been designed to capture and leverage the knowledge of an enterprise to generate dynamic business rules by which an effective procurement strategy can be generated based on enterprise needs and the analysis of relevant market conditions. The performance and practices of the suppliers can also be evaluated and captured continuously, respectively, by the AOKBS. Artificial intelligence technologies such as case‐based reasoning and agent technology are incorporated for decision analysis. A prototype e‐procurement system has been built and implemented successfully with a multinational manufacturer.

Recent advances related to on‐line analytical processing (OLAP) have resulted in a significant improvement in data analysis efficiency by virtue of its multidimensional database structure and pre‐computing operations of measuring data. However, the research related to the design and implementation of OLAP, particularly in the support of dispersed manufacturing networks in terms of ‘intelligent decision making’, has yet to be considered as remarkable. Research studies indicate that the level of intelligence of decision support systems can be enhanced with the incorporation of computational intelligence techniques such as case‐based reasoning or rule‐based reasoning. This paper describes the development of an intelligent data‐mining system using a rule‐based OLAP approach which can be adopted to support dispersed manufacturing networks in terms of performance enhancement. In this paper, the techniques, methods and infrastructure for the development of such a data‐mining system, which possesses certain intelligent features, are presented. To validate the feasibility of this approach, a case example related to the testing of the approach in an emulated industrial environment is covered.

Product development stages. The analytic hierarchy process (AHP), which breaks down a complex problem into simple hierarchical decision‐making processes, can be incorporated with fuzzy logic to suggest the relative strength of the factors in the corresponding criteria, thereby enabling the construction of a fuzzy judgement matrix to facilitate decision‐making. This paper proposes a fuzzy AHP approach in modular product design complemented with a case example to validate its feasibility in a real company. Test findings indicate that the approach is helpful for providing key decision support information in terms of product module selection during product development stages. The significance of the contribution of this paper is the suggestion of a novel approach in modular product design, embracing a combination of computational intelligence and traditional techniques, thereby providing more alternatives and ideas for those researchers who are interested in this field of study.