data envelopment analysis (dea) is a method for measuring the relative efficiencies of a set of decision-making units (dmus) that use multiple inputs to produce multiple outputs. in this paper, we study the measurement of dmu performances in dea in situations where input and/or output values are given as imprecise data. by imprecise data we mean situations where we only know that the actual val...

Keywords: Fuzzy DEA Intuitionistic fuzzy DEA Optimistic and pessimistic performance Ranking methods Bank branch efficiency a b s t r a c t Intuitionistic fuzzy set (IFS) is an extension of fuzzy set and an approach to define a fuzzy set where available information is not sufficient to define an imprecise concept by means of a conventional fuzzy set. The existing fuzzy DEA (FDEA) models for meas...

addressing deficit irrigation scheduling (dis) for strategic crop production (especially wheat) under precipitation uncertainty is a priority for irrigation scheduling in drought conditions. this research investigated the precipitation uncertainty by enacting optimistic and pessimistic scenarios for the next 20 years by considering the statistical record of climate in badjgah area. dis was cond...

Binary possibilistic utility unifies two previously proposed qualitative decision models: optimistic and pessimistic utilities. All these decision models have been axiomatized in a von Neumann-Morgenstern setting. These axiomatizations have shown the formal similarity of these qualitative utilities and expected utility. Unfortunately in this framework, the representation of uncertainty has to b...

 Recently, Khodabakhshi and Aryavash have introduced a ranking method [Applied Mathematics Letters, 25 (2012) 2066-2070.], which is based on an optimistic-pessimistic approach of data envelopment analysis (DEA). This method ranks all decision making units according to a combination of their minimum and maximum possible efficiency scores which are determined by solving two linear programming mo...

The designs of software transactional memory (STM) algorithms to date have been optimistic: transactions that run into inconsistencies abort and retry. The common view is that this optimistic approach gives significant performance benefits, and yet we know that it also results in complex programming, limitations on what can be executed within a transaction, and difficult debugging. This is a bu...