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

Distribution network design as a strategic decision has long-term effect on tactical and operational supply chainmanagement. In this research, the location-allocation problem is studied under demand uncertainty. The purposesof this study were to specify the optimal number and location of distribution centers and to determine theallocation of customer demands to distribution centers. The main feature of this research is solving the model withunknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set ofpossible scenarios for customer demands is created based on the Monte Carlo simulation. The coefficient ofvariation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network isdefined based on the concept of robust optimization. The best structure is identified using genetic algorithms and14% reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created byfluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logisticalsystem. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.