One of the important issues in data envelopment analysis (DEA) is sensitivity analysis. This study discusses about deleting some of the inputs and outputs and investigates the influence of it on efficiency status of Decision Making Units (DMUs). To this end some models are presented for recognizing this influence on efficient DMUs. Model 2 (Model 3) in section 3 investigates the influence of deleting i(th) input (r(th) output) on an efficient DMU. Thereafter these models are improved for deleting multiple inputs and outputs. Furthermore, a model is presented for recognizing the maximum number of inputs and (or) outputs from among specified inputs and outputs which can be deleted, whereas an efficient DMU preserves its efficiency. Finally, the presented models are utilized for a set of DMUs and the results are reported.

In many application to wish data envelopment analysis could be applied there is often a fixed or common cost , wich is imposed on all decision making units (DM U s ).In proposed approach by Cook and kress (1999)of equitable allocation of shared costs between all DM U s several problems should be solved which gives rise to commputational difficulties. G. r. Jahanshahloo et al.presened an approoch in which without solving linear programmin problems only using simple formulas the equitable allocation is achieved. In this paper this approach is imposed on decision making units (DM U s) with fuzzy inputs and outputs in the case that fixed costs are fuzzy.

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This paper provides a new sight of dynamic data envelopment analysis (DDEA) in dependence of investment activities or link factors. However dynamic DEA proposed by Färe and Grosskpf regards the connectivity between two assessment periods, some link factors that bear consecutiveness between two assessment periods (namely, link factors) have not been noted. Then in this paper we aim to clarify those link factors in details and based on them, we develop a new DDEA framework. To do this all factors that may affect on a DMU's process in an assessment period are studied. These factors may be from another assessment period, i.e. be carry-over. Then based on these factors, a new DDEA framework is developed and then the important concept of efficiency measure of DMUs is developed in this new DDEA framework.

Heretofore one of the issues which has not considered in DEA sensitivity analysis is modification in the number of decision making units. In this paper some of DMUs are deleted and the influence of it (deleting one or multiple DMUs) is examined. At first two systems are presented for determining the influence of deleting one or multiple DMUs on efficiency status of other DMUs. Then a model is presented for minimizing the number of deleted DMUs such that an inefficient unit becomes efficient. Finally the presented model and systems are utilized for a set of DMUs and the results are reported.