Page 184 - Materials Chemistry, Second Edition

182 9. Life cycle decision support framework: Method and case study

Except for the uncertainty considered in MCDM, MCDM used in further analysis of alter-
natives based on LCSA results has adapted the group decision making concept. The group
MCDM can acquire all preferences from multiple stakeholders and reflect them in the
decision-making results. For example, group interval BWM and interval TOPDIM have been
adapted in hydrogen production technique selection (Ren et al., 2018), group interval AHP
has been proposed in sustainability assessment framework (Ren et al., 2017), and group
GRA has been raised for hydrogen technologies selections as well (Manzardo et al., 2012).
To illustrate better the process of MCDM based on LCSA, a case study regarding oil man-
agement is studied by a group fuzzy MCDM method in the next section.

9.3 Methodology

In this section, a new group MCDM method is introduced. In order to deal with hesitations
happened during the judgment process by decision makers, the ZBWM (Aboutorab et al.,
2018) was adapted and revised as the weighting method to transform the linguistic prefer-
ences of criteria with respect to different decision makers into numerical fuzzy criteria
weights. In this revised version, group opinions were considered with various opinions from
multiple stakeholders. Thereafter, the goal programming method was adapted as the aggre-
gating method to help aggregate integrated criteria preference and the actual performance of
each option. Those options can be ranked according to the scores generated from the pro-
posed method. The detailed methodology, calculation process, and result discussions were
provided accordingly below.

9.3.1 Step 1. Determine the criteria system
The first step is to select criteria from LCSA indicators that can better describe the options.
Criteria in environmental, economic, and social perspectives are selected from LCA, LCC, and
LCSA, respectively, and the hierarchical structure of the criteria system is built in this step. The
selection of criteria depends on the situation of industry and the knowledge from experts.

9.3.2 Step 2. Determine the decision-making matrix
As for all alternatives (a 1 , a 2 , …, a n ), the decision-making matrix (X) contains the informa-
tion of data (x ij )of ith criterion with respect to jth alternative where i¼1, 2, …, m, and j¼1,
2, …, n, as shown by Eq. (9.1).
The data x ij of criteria are collected from scientific and reliable information resources, for
instance, the LCA, LCC, and SLCA results with the same research boundary. The criteria
weights indicate the importance of each criterion in the analysis. To avoid large changes in
scores due to the unit of weights; the weights (w i )of ith criterion, as shown in Eq. (9.1), should
satisfy Eq. (9.2).
X
w j ¼ 1 (9.2)
i