Demand Among all related activities, selection of a

Demand for spare parts are usually
intermittent, thus the requirement can arise at any time whenever a component
fails or needs a proper maintenance (Wang & Syntetos,
Identification of a suitable location to place a DC is heavily influenced by
the demand for a given product (Nozick &
Turnquist, 2001a)
and its characteristics. Even though the products which have a lower demand are
often held in centralized locations (Nozick &
Turnquist, 2001a),
merchandises such as vessel spares which possesses an erratic nature in demand
and storage restrictions on-board (Jiang & Kong,
must also be stored in centralized DCs to ensure the availability, providing a
smooth flow of supply. Deficiency in provision of required spare parts at the
right time causes improper maintenance resulting break downs and finally
enhances the lead time of the vessel generating substantial losses in the
revenue (Hmida et al., 2013).

According to the systematic view of
supply chain management the ultimate goal should be the customer satisfaction
thus in the perspective of a vessel owner, having a fully functioning vessel
becomes the priority (Hmida et al., 2013). Among all related activities, selection of a
facility location can be considered as one of the most critical decisions made
in supply chain design, in order to maintain an uninterrupted flow of a product
(Hu, Wu, & Cai,
Facilities such as warehouses, factories, DCs etc. must be located
strategically to attain the objective of exploiting supply chain performance
and profitability while optimizing logistical network arrangement (Hu et al., 2009). Nearly a decade ago the main goal was to minimize
the transportation cost (Chen, 2001) but in recent researches (Hmida et al., 2013) many influential factors have been identified for
the selection of a suitable site for a facility.

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 (Chen, 2001) has introduced a new fuzzy multiple
criteria group decision making method
as an approach to select the most suitable location for a facility. It is
further emphasized the importance of giving weights of criteria in linguistic
variables where fuzzy numbers and linguistic variables are used to evaluate
both the linguistic and numerical data. The number of levels of linguistic
values can be determined based on the need of intellectual perspectives and
characteristics of collected data. This method gives a systematic approach to
decision makers to make the right decision in a fuzzy environment. An updated
and a revised version of Chen’s idea, The Fuzzy TOPSIS method is presented in (Hu et al., 2009)
as a solution for the inability of the conventional methods to measure
fuzziness or uncertainty. This can be considered as a scientific and an
efficient method in which the decision criteria is mainly divided into
quantitative and qualitative criteria and the ratings of qualitative criteria
is evaluated in linguistic variables which are defined by triangular fuzzy
numbers. Furthermore (Liu, Chan, & Chung, 2011) propose a hybrid algorithm which
combines rough sets and inter-active multi objective fuzzy decision theory to obtain comprehensive evaluation values of the
alternative choices considered during the study, generating a multi-objective
optimization model. It is proven that this method can be used for facility
location decision making problems, which have large number of variables. Even
though the concept of facility location is being widely studied by researches
for many years which are applicable to many industries, the Shipping industry remains
unfocused, hence this particular study aims on developing a model considering
the factors specifically related to the Ship Supply Logistics enabling its
direct usage for industrial practices.