Hybrid Case-based Reasoning Approach Research Articles

A hybrid case-based reasoning approach to detecting the optimal solution in nurse scheduling problem

A hybrid case-based reasoning approach to detecting the optimal solution in nurse scheduling problem

A New Hybrid Case-Based Reasoning Approach for Medical Diagnosis Systems

Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.

Applying hybrid case-based reasoning in agent-based negotiations for supply chain management

In recent years, enormous research effort has been spent on the development of multi-agent systems to automate buyer–seller negotiations in supply chain management (SCM) applications. In many of these agent-based negotiation systems, the negotiation process is restricted to the stage when agents interact to exchange bargaining offers; activities in the pre-bargaining and post-bargaining stages are ignored. With a more comprehensive perspective, the negotiation lifecycle comprises a number of phases including the pre- and post-bargaining phases, in addition to the phase when participants interact to bargain. In this paper, a hybrid case-based reasoning approach is applied in the pre- and post-negotiation phases to support adaptive negotiation strategy for buyer–seller negotiations in SCM applications. When a new negotiation problem arrives, a similar previous negotiation case is retrieved from the case database and its solutions are recommended for inferring the negotiation parameters. The recommended parameters have to be adjusted to suit the new negotiation problem. Subsequent to negotiation completion, negotiation parameters and the negotiation outcome are analyzed and evaluated. After the evaluation, the new negotiation case, including the problem descriptions and solutions, is retained in the case database for future applications.

Design of a generic product and process development system a hybrid case-based reasoning approach

During the New Product Development (NPD) cycle, it is important to focus and integrate relevant information into the stages of product conceptualisation, design and development so that they can be used for product and process design effectively. In this paper, a Generic Product and Process Development System (GPDS), which integrates knowledge-based technologies of Rule-Based Expert System (RBES) and Case-Based Reasoning (CBR), is proposed to facilitate product and process development. Although RBES is adopted for sorting product design criteria, relevant past cases are retrieved by the CBR system during product and process design stages. In doing so, previous knowledge in terms of rules and past practices will be made use of during the product and process design stages, resulting in an enhancement of the efficiency of design and operations. By applying GPDS in Hasbro Fast East Limited, the new product design life cycle is greatly shortened with significant reduction in the total development cost.

Application of a hybrid case-based reasoning approach in electroplating industry

Case-Based Reasoning (CBR), a well known Artificial Intelligence (AI) technique, has already proven its effectiveness in numerous industries. In this research, we try to adopt CBR technique in electroplating industry where the final products are electroplated accessory of watches. In order to ensure sufficient profit margin for electroplating manufacturer, it is important to grasp the coating weight of electroplating component accurately so that salespersons can make sure their quotation prices cover the precious metal cost. Apart from quotation accuracy, responsiveness is also a critical competitive edge in electroplating industry. In this connection, developing a quick response decision-making system with considerably reliable price is what electroplating industry needs. To cope with this problem, a hybrid CBR system combined with Rule-based Reasoning (RBR) and Fuzzy Logic (FL) concepts is established. Such system is capable to convert knowledge from experienced staff; simulate the ‘mind-set’ of decision maker in solving problem through acquisition of specific knowledge and experience; and build up self-learning characteristics. Moreover, this research interprets cases as some objective selection rules, putting CBR in a position much closer to RBR. This innovative concept differentiates from previous CBR researcher work, and will be explained through a practical example. Further, this research also suggested that it is very difficult and not practical to develop a pure CBR system. Applying some subjective guiding rules in CBR can significantly improve the performance of system in the early learning stage.