inventory optimization with chance-constrained programming under demand uncertainty

Uncertainty and variability in demand and supply processes make it difficult for companies to make inventory management decisions. in this study, a model is developed that will provide the maximum service level of a pharmaceutical warehouse under the budget constraint, taking into account stochastic demand. due to stochastic demand, the chance constraint programming approach is used to achieve the desired service level at different levels. in this study, the problem of a pharmaceutical warehouse that supplies medicines to pharmacies and hospitals is considered as a real-world problem. the model is designed as a dynamic programming model based on periods. since there are thousands of drugs in the pharmaceutical warehouse, as the number of products increases, it becomes difficult to find the appropriate solution in an acceptable time. the model is first solved as a mixed integer linear programming model in lingo. a genetic algorithm (ga) approach is then proposed for large-scale problems. the simulation optimization method also applied to the problem and compared with the optimization method and ga. the ga approach yields better results in the shortest time as the number of periods increases. the developed integrated model demonstrated a numerical example in a pharmaceutical warehouse and was solved using three different approaches. this study is of great importance in terms of providing results that will enable managers to decide the amount of items they should keep in their warehouses by using their budgets in the most efficient way. nine different scenarios have been derived with various chance constraint risk factors and budget values. scenario analysis has revealed that the budget has a significant impact on the results at a 95% confidence level. if a pharmaceutical warehouse increases its budget by 10%, it can reduce its total annual inventory carrying costs by 70%.