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 strategicdecision has long-term effect on tactical and operationalsupply chain management. In this research, the location–allocation problem is studied under demand uncertainty.The purposes of this study were to specify the optimalnumber and location of distribution centers and to determinethe allocation of customer demands to distributioncenters. The main feature of this research is solving themodel with unknown demand function which is suitablewith the real-world problems. To consider the uncertainty,a set of possible scenarios for customer demands is createdbased on the Monte Carlo simulation. The coefficient ofvariation of costs is mentioned as a measure of risk and themost stable structure for firm’s distribution network isdefined based on the concept of robust optimization. Thebest 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 diseasesoutbreak in some areas of the country) to the logisticalsystem. It is noteworthy that this research is done inone of the largest pharmaceutical distribution firms in Iran.