infrastructure, transportation and distribution systems, cultural issues, and quality
of services. In that research, the weighted fuzzy factor rating system (FRS) was used
as a methodology to evaluate places. Hu et al. (2009) suggested a hybrid methodology including the fuzzy set theory, simple additive weighing (SAW) method, and
TOPSIS to select the best location of distribution centers. They solved this problem
in a group decision-making environment. To this end, in that research, first, the
weight of criteria including price, market, transportation, and after service was
determined by SAW method, and then the rank of places was calculated by TOPSIS.
Ji and Huailin (2009) employed genetic algorithm (GA) and AHP to determine the
best location of logistics distribution centers. Based on the proposed method, at first,
places with minimum cost were screened by GA, and then the rank of locations
considering environmental and service factors was computed by AHP. Wang et al.
(2010) in a research determined the best location of logistics distribution centers by
using AHP. In that research, 16 criteria in 5 categories including raw material
availability, human resource, distribution network, availability of infrastructure,
and close proximity to market were used to assess alternatives’ performance. The
results of that paper indicated that degree of proximity in the raw material availability category was the main factor in selecting the best location of distribution
centers. Demirel et al. (2010) in a study selected the optimal location of distribution
warehouses for one of the major logistics companies in Turkey. They applied the
Choquet integral method as a methodology in that research. To assess candidate
places in that research, 16 criteria under 5 dimensions including cost, labor characteristics, infrastructure, market, and environmental are considered.
Kuo (2011) applied multi-criteria decision-making methods to select the best
location of international distribution centers in Taiwan. In their study, first, the
relationship between criteria was identified by decision-making trial and evaluation
laboratory (DEMATEL), and then the rank of places was determined through three
methods including TOPSIS, AHP, and analytic network process (ANP). Imports and
exports volume, port rate, location resistance, information abilities, port and warehouse facilities, port operation system, extension transportation convenience, and
density of shipping line were the criteria employed to assess alternatives. Awasthi
et al. (2011) suggested fuzzy TOPSIS as a methodology to select the optimal
location of urban distribution centers in a logistic company. Alternatives in that
research were analyzed by 11 criteria such as accessibility, connectivity to multimodal transport, cost, environmental impact, proximity to customers, resource
availability, and possibility of expansion. Using the axiomatic fuzzy set (AFS) and
TOPSIS, Wang et al. (2012) ranked candidate places for urban distribution centers in
a logistic company in China. Sixteen criteria categorized into six dimensions including natural environment, transportation, business environment, candidate land,
supply condition, and environmental impact were used to analyze alternatives in
that research.
Ashrafzadeh et al. (2012) determined the optimal location of distribution warehouses for an Iranian company using fuzzy TOPSIS. Alternatives in that research
were assessed by 15 criteria such as labor cost, transportation cost, handling cost,
land cost, skilled labor, availability of labor force, proximity to customers and lead
76
S. Kheybari and A. Pooya
of services. In that research, the weighted fuzzy factor rating system (FRS) was used
as a methodology to evaluate places. Hu et al. (2009) suggested a hybrid methodology including the fuzzy set theory, simple additive weighing (SAW) method, and
TOPSIS to select the best location of distribution centers. They solved this problem
in a group decision-making environment. To this end, in that research, first, the
weight of criteria including price, market, transportation, and after service was
determined by SAW method, and then the rank of places was calculated by TOPSIS.
Ji and Huailin (2009) employed genetic algorithm (GA) and AHP to determine the
best location of logistics distribution centers. Based on the proposed method, at first,
places with minimum cost were screened by GA, and then the rank of locations
considering environmental and service factors was computed by AHP. Wang et al.
(2010) in a research determined the best location of logistics distribution centers by
using AHP. In that research, 16 criteria in 5 categories including raw material
availability, human resource, distribution network, availability of infrastructure,
and close proximity to market were used to assess alternatives’ performance. The
results of that paper indicated that degree of proximity in the raw material availability category was the main factor in selecting the best location of distribution
centers. Demirel et al. (2010) in a study selected the optimal location of distribution
warehouses for one of the major logistics companies in Turkey. They applied the
Choquet integral method as a methodology in that research. To assess candidate
places in that research, 16 criteria under 5 dimensions including cost, labor characteristics, infrastructure, market, and environmental are considered.
Kuo (2011) applied multi-criteria decision-making methods to select the best
location of international distribution centers in Taiwan. In their study, first, the
relationship between criteria was identified by decision-making trial and evaluation
laboratory (DEMATEL), and then the rank of places was determined through three
methods including TOPSIS, AHP, and analytic network process (ANP). Imports and
exports volume, port rate, location resistance, information abilities, port and warehouse facilities, port operation system, extension transportation convenience, and
density of shipping line were the criteria employed to assess alternatives. Awasthi
et al. (2011) suggested fuzzy TOPSIS as a methodology to select the optimal
location of urban distribution centers in a logistic company. Alternatives in that
research were analyzed by 11 criteria such as accessibility, connectivity to multimodal transport, cost, environmental impact, proximity to customers, resource
availability, and possibility of expansion. Using the axiomatic fuzzy set (AFS) and
TOPSIS, Wang et al. (2012) ranked candidate places for urban distribution centers in
a logistic company in China. Sixteen criteria categorized into six dimensions including natural environment, transportation, business environment, candidate land,
supply condition, and environmental impact were used to analyze alternatives in
that research.
Ashrafzadeh et al. (2012) determined the optimal location of distribution warehouses for an Iranian company using fuzzy TOPSIS. Alternatives in that research
were assessed by 15 criteria such as labor cost, transportation cost, handling cost,
land cost, skilled labor, availability of labor force, proximity to customers and lead
76
S. Kheybari and A. Pooya