Abstract
Simulation modelling is a complex decision-making process that involves the processing of various knowledge and information within a context defined by specific application. Building a “good” simulation model has been heavily reliant on the skill and experience of human expert, which has become one of the most expensive and limited resources in market competition. Case-based reasoning (CBR) can be used to effectively solve problems in ill-defined domains where operations specific knowledge and information are processed in a contextual manner such as simulation modeling. This paper addresses some of the basic issues in applying CBR to improve simulation modeling, with emphasis on knowledge or case representation, case indexing, and case matching. Numerical examples and experimental studies were conducted to verify and validate the concepts and model/algorithms developed. The results showed the effectiveness and applicability of proposed method.
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