Resilient Supplier Selection in Logistics 4.0 with Heterogeneous
  Information
  Supplier selection problem has gained extensive attention in the prior
studies. However, research based on Fuzzy Multi-Attribute Decision Making
(F-MADM) approach in ranking resilient suppliers in logistic 4 is still in its
infancy. Traditional MADM approach fails to address the resilient supplier
selection problem in logistic 4 primarily because of the large amount of data
concerning some attributes that are quantitative, yet difficult to process
while making decisions. Besides, some qualitative attributes prevalent in
logistic 4 entail imprecise perceptual or judgmental decision relevant
information, and are substantially different than those considered in
traditional suppler selection problems. This study develops a Decision Support
System (DSS) that will help the decision maker to incorporate and process such
imprecise heterogeneous data in a unified framework to rank a set of resilient
suppliers in the logistic 4 environment. The proposed framework induces a
triangular fuzzy number from large-scale temporal data using
probability-possibility consistency principle. Large number of non-temporal
data presented graphically are computed by extracting granular information that
are imprecise in nature. Fuzzy linguistic variables are used to map the
qualitative attributes. Finally, fuzzy based TOPSIS method is adopted to
generate the ranking score of alternative suppliers. These ranking scores are
used as input in a Multi-Choice Goal Programming (MCGP) model to determine
optimal order allocation for respective suppliers. Finally, a sensitivity
analysis assesses how the Suppliers Cost versus Resilience Index (SCRI) changes
when differential priorities are set for respective cost and resilience
attributes.
