Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry


Department of Industrial Engineering, Amirkabir University of Technology, Hafez Street, NO 424, 15875-4413, Tehran, Iran


Distribution network design as a strategic
decision has long-term effect on tactical and operational
supply chain management. In this research, the location–
allocation problem is studied under demand uncertainty.
The purposes of this study were to specify the optimal
number and location of distribution centers and to determine
the allocation of customer demands to distribution
centers. The main feature of this research is solving the
model with unknown demand function which is suitable
with the real-world problems. To consider the uncertainty,
a set of possible scenarios for customer demands is created
based on the Monte Carlo simulation. The coefficient of
variation of costs is mentioned as a measure of risk and the
most stable structure for firm’s distribution network is
defined based on the concept of robust optimization. The
best structure is identified using genetic algorithms and
14 % reduction in total supply chain costs is the outcome.
Moreover, it imposes the least cost variation created by
fluctuation in customer demands (such as epidemic diseases
outbreak in some areas of the country) to the logistical
system. It is noteworthy that this research is done in
one of the largest pharmaceutical distribution firms in Iran.