Data Envelopment Analysis with Non-Homogeneous DMUs

摘要: Data envelopment analysis (DEA), as originally proposed is a methodology for evaluating the relative efficiencies of set homogeneous decision making units (DMUs) in sense that each uses same input and output measures (in varying amounts from one DMU to another). In some situations, however, assumption homogeneity among DMUs may not apply. As an example, consider case where are plants industry which all produce products. Evaluating absence gives rise issue how fairly compare other units, be exactly ‘business’. A related problem, has been examined extensively literature, missing data problem; produces certain output, but its value known. One approach taken address this problem ‘create’ (e.g. substituting zero, or by taking average known values), use it fill gaps. present setting, isn’t DMUs, rather produced. We argue herein if chosen any reason cannot therefore does put resources place do so, then would inappropriate artificially assign zero ‘average’ nonexistent factor. Specifically, desire evaluate what does, than penalize credit doesn’t do. current chapter we DEA-based models requirement relaxed. these examine manufacturing plants.