Page 272 - Materials Chemistry, Second Edition

270 13. Multi-criteria decision-making after life cycle sustainability assessment under hybrid information

aspects. However, the results of LCSA cannot answer one of the most common questions
of the users: which is the most sustainable scenario among these alternatives? This is because
the users must face a set of conflict criteria when selecting the most sustainable energy
and industrial system among various alternatives. Accordingly, LCSA is combined with
multi-criteria decision analysis, also called “multi-criteria decision-making,” for ranking
the alternative energy and industrial systems according to their sustainability performances.
For instance, Ren et al. (2015b) combined LCSA with AHP (analytic hierarchy process) and
VIs ˇekriterijumsko KOmpromisno Rangiranje (VIKOR) for sustainability prioritization of
three bioethanol production pathways (corn-based, wheat-based, and cassava-based).
Xu et al. (2017) combined LCSA with the vector-based three-dimensional algorithm and
AHP for ranking three alternative ammonia production processes.
All these studies ranked the energy and industrial systems based on the condition that all
the data with respect to the evaluation criteria are crisp numbers (fuzzy numbers were
transformed into crisp numbers). In addition, many methods for achieving life cycle sustain-
ability ranking under uncertainties were developed. Ren et al. (2017a, b) developed an
improved weighting method and an extended extension theory for ranking energy and
industrial systems under uncertainties. Ren (2018a) employed the fuzzy two-stage logarith-
mic goal programming method and the interval grey relational analysis method for determin-
ing the sustainability sequence of four electricity generation systems. Ren et al. (2018)
developed an interval best-worst method for determining the weights of the criteria for sus-
tainability assessment based on the opinions of multiple stakeholders and developed an
interval multi-criteria decision-making method for sustainability ranking of industrial sys-
tems, which address the decision-making matrix composed by using interval numbers.
Moreover, there also some studies focusing on developing some methods for achieving life
cycle sustainability ranking of alternatives when the users do not have the real data of the
alternatives with respect to the evaluation criteria and all the data were based on the
judgments of the decision-makers/stakeholders.
Manzardo et al. (2012) employed the improved grey relational analysis to select the most
sustainable scenario among twelve hydrogen production technologies, and all the data
(relative performances) of these technologies with respect to the evaluation criteria were
determined based on the judgments of the experts. Onat et al. (2016) employed the
intuitionistic fuzzy TOPSIS (technique for order of preference by similarity to ideal solution)
to rank seven alternative vehicle technologies. Based on the above-mentioned analysis, there
is still a great challenge to be overcome, because LCSA usually involves multiple types of
information besides data uncertainty problems, and linguistic variables corresponding to
fuzzy numbers were also usually used to describe the relative performances of the alterna-
tives with respect to some “soft” criteria, the data to which cannot be quantified directly.
The crisp numbers and the interval numbers are usually used in LCC and LCA for the
“hard” criteria, the linguistic variables corresponding to intuitionistic fuzzy numbers are
employed to describe the relative performances of the energy and industrial systems with
respect to the “soft” criteria.
Besides the introduction section, the remaining parts of this study have been organized
as follows: the developing multi-criteria decision-making method under multi-type data
condition is developed in Section 13.2; an illustrative case is studied in Section 13.3; sensitivity
analysis is carried out in Section 13.4; and finally, this study is concluded in Section 13.5.