In this paper, we deal with fuzzy random variables for inputs andoutputs in Data Envelopment Analysis (DEA). These variables are considered as fuzzyrandom flat LR numbers with known distribution. The problem is to find a method forconverting the imprecise chance-constrained DEA model into a crisp one. This can bedone by first, defuzzification of imprecise probability by constructing a suitablem...

I many circumstances, evaluations are based on empirical data. However, some observations may be imprecise, meaning that it is not entirely clear what occurred in them. We address the question of how beliefs are formed in these situations. The individual in our model is essentially a “frequentist.” He first makes a subjective judgment about the occurrence of the event for each imprecise observa...

On account of the existence of uncertainty, DEA occasionally faces the situation of imprecise data, especially when a set of DMUs include missing data, ordinal data, interval data, stochastic data, or fuzzy data. Therefore, how to evaluate the efficiency of a set of DMUs in interval environments is a problem worth studying. In this paper, we discussed the new method for evaluation and ranking i...

A traditional theory of uncertainty says that beliefs come in degrees. Degrees of belief (“credences”) have real number values between  and , where  conventionally represents certain belief,  represents certain disbelief, and values in between represent degrees of uncertainty. We have elegant, well-understood normative theories for credences: norms for how credences should hang together at ...

This introductory chapter provides definitions of sustainability, sustainable development, decoupling, and related terms; gives an overview of existing interdisciplinary research fields related to ICT for Sustainability, including Environmental Informatics, Computational Sustainability, Sustainable HCI, and Green ICT; introduces a conceptual framework to structure the effects of ICT on sustaina...

in this paper, we deal with fuzzy random variables for inputs andoutputs in data envelopment analysis (dea). these variables are considered as fuzzyrandom flat lr numbers with known distribution. the problem is to find a method forconverting the imprecise chance-constrained dea model into a crisp one. this can bedone by first, defuzzification of imprecise probability by constructing a suitablem...