Case-based reasoning
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
A tutorial on case based reasoning
Soft computing in case based reasoning
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Weights determination of OWA operators by parametric identification
Mathematics and Computers in Simulation
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
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Industrial decision-makers often try to develop new products by reusing their past experience. Methods for obtaining feedback are available in knowledge management and produce tangible results in the industrial world. Nowadays, companies often consider employees' knowledge as an asset. This approach is a potential answer for reusing knowledge acquired through experience. The Case-Based Reasoning (CBR) methodology, based on cognitive sciences, consists in solving new problems by reusing past experience. Case-Based Reasoning is an approach to solving a new problem by remembering and adapting a previous successful similar situation to the problem at hand. Of the four activities comprising CBR - Retrieve, Reuse, Revise, and Retain-this paper deals with the ''Retrieve'' phase. Consequently, we suggest using the Multi-Criteria Decisions concept in problem description to search for the solution in a case-based scenario. We show that Multi-Criteria Decisions and Case-Based Reasoning are complementary. This paper proposes using knowledge acquisition as a basis for seeking solutions from non-compensatory multi-criteria decision aids such as the ELECTRE-I and ELECTRE-II methods. We also carry out a robustness analysis at the end of this paper. We describe an industrial application with wire forming machines as an illustration of our recommendation.