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.