Side effects of absolute weight bounds in data envelopment analysis models

This paper analyses effects of incorporating absolute weight bounds for input and output weights in the classical models of data envelopment analysis (DEA). It is shown that a DEA model with such restrictions may not maximise the relative efficiency of the assessed decision making unit (DMU) and may not find the set of weights representing the assessed DMU in the best light in comparison to the other DMUs. Consequently, it may produce misleading target values for an inefficient DMU and a wrong reference set of efficient peers. A way of avoiding these ‘side effects’ is based on a utilisation of a maximin DEA model, equivalent to the classical DEA model if no additional restrictions are imposed.