The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independently of the other mThe conventional constant and variable returns-to-scale models of data11 1envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be reduced independently of the other measures. In this paper we argue that this assumption may not be suitable in applications in which some inputs or outputs are closely related to each other. Assuming strong disposability of such closely related measures may lead to unrealistic input and output profiles, and result in meaningless efficiency scores. Examples include inputs and outputs that are strongly correlated, represent overlapping measures or situations in which one measure is a subset of another. In this paper we develop production technologies that allow the specification of groups of closely related inputs and outputs which are only jointly weakly disposable. This assumption does not change the existing proportions between the closely related measures in the same group. We demonstrate the usefulness of the suggested approach by computational experiments.The conventional constant The conventional constant The conventional constant The conventional constant and variableThe conventional constant and variable returns-to-scale mThe conventional constant and variable returns-to-scale models of data envelopment analysis (DEA) incorporate the assumption of strong, or free, disposability. According to this assumption, each input can be increased and each output can be 1reduced independe
