Abstract
A balanced scorecard (BSC) is a management decision tool intended to be the corporate performance measurement. It also can play an important role in transforming an organization’s mission and strategy into a balanced set of integrated performance measures. Assigning suitable weight to each level of balanced scorecard is crucial to conduct performance evaluation effectively. In this research a case-based reasoning (CBR) system has been developed to assist in assigning the suitable weights. Based on the balanced scorecard design, this study proposed a three-level feature weights design to enhance CBR’s inference performance. For effective case retrieval, a genetic algorithm (GA) mechanism is employed to facilitate weighting all of levels in balanced scorecard and to determine the most appropriate three-level feature weights. The proposed approach is compared with the equal weights approach and the analytical hierarchy process (AHP) approach. The results indicate that the GA-CBR approach is able to produce more effective performance measurement.
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