Two biggest limitations in the use of SDSS are illogical weighting
systems and lacking proper ways to interpret the alternatives. To
overcome these restrictions and improve the modeling environment,
the proposed SDSS added CFA and detailed value estimation.
CFA offered a more analytic procedure to group the input variables
with statistical significance, and made it possible to reduce 15
variables into five factors. Another advantage of using groups
instead of a large number of inputs stands on its increased sensitivity.
Detailed value estimation suggested a possible way to
examine the alternatives and provided a foundation to select the
best possible solution with the given variables. Unlike the overall
suitability scores, specific value estimation for each factor illustrated
different results with more comprehensive perspectives on
the final results. Therefore, the proposed SDSS adequately answers
the two research questions. Using CFA created a reliable ground to
the variable grouping process, and the routes showed different
trajectory based on their optimum variables, which answers the
research question 1. The suitability matrix and detailed values
estimated different scores for corresponding routes with their
emphasized variables responding to the question 2