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Prioritization of biofuels production pathways under uncertainties 339

MCDM, intuitional fuzzy MCDM, and stochastic MCDM. Interval
MCDM takes the interval number as the input data in the decision process,
so that the value of each criterion reflects its maximum and minimum values
(Tsaur, 2011). For example, Giove (2002) and Jahanshahloo et al. (2006)
have extended TOPSIS into interval TOPSIS. Fuzzy MCDM is the
MCDM that bringing fuzzy number such as triangular fuzzy number and
trapezoidal fuzzy number into consideration (Pohekar and Ramachandran,
2004; Kahraman, 2008). The fuzzy number consists more information than
interval number to express the uncertainty (Pohekar and Ramachandran,
2004; Kahraman, 2008). For instance, Sevkli (2010) extended ELECTRE
into fuzzy ELECTRE, Shemshadi et al. (2011) extended VIKOR into fuzzy
VIKOR, and Liu and Wang (2007) developed a MCDM based on intuitio-
nistic fuzzy. The fuzzy MCDM has been further extended into intuitional
fuzzy MCDM which adds degree of nonmembership to better express the
uncertainty (Liu and Wang, 2007). Different from fuzzy MCDM, stochastic
MCDM uses randomness to reveal the uncertainty sets (Ramanathan, 1997).
For example, Xiong and Qi (2010) have extended TOPSIS into stochastic
multicriteria decision making method. Furthermore, many researchers have
also tried to combine different MCDM methods under uncertainty to form
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hybrid MCDM. For example, B€uy€uk€ozkan and Cifci (2012) have proposed
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a hybrid MCDM approach combining fuzzy Decision making trial and eval-
uation laboratory (DEMATEL), fuzzy (Analytic Network Process) ANP,
and fuzzy TOPSIS together.
Some researchers have done studies to compare biofuel production pro-
cesses sustainably (e.g., Ou et al., 2009; Yang et al., 2011; Larkum et al.,
2012), but these researches cannot show the direct priority through the
comparisons. Some people helped to make choices in this selection problem
(e.g., Schaidle et al., 2011; Vlysidis et al., 2011; Sharma et al., 2011), but they
did not consider uncertainties in the biofuel production processes. Further-
more, some scholars have studied energy selection problem but have not
specifically scoped to biofuel such as Sadeghi et al. (2012) provided a fuzzy
MCDM approach for renewable electricity production and Lee et al. (2009)
have conducted a fuzzy AHP method for energy technology prioritization.
Therefore we shall bring uncertainties into consideration and provide a sci-
entific and comprehensive analysis for biofuel production pathways priori-
tization. Because the interval number represents the uncertainty of the data
biofuel production process and leads to few obstacles in data collection, so
the data type of interval MCDM is more in line with the operation of sus-
tainable biofuel production selection. The interval AHP which allows the