A simulator that uses Tabu search to approach the optimal solution to stochastic inventory models

Inventory is one of the most expensive and significant assets for many enterprises. When we face stochastic inventory models, we find that there is no analytical solution to most of them due to the enormous complexity of the expressions that represent the total cost. We present in this paper an alternative method to emulate and approximate the optimal solution of some stochastic inventory models. This is a system that uses a combination of simulation and the Tabu Search metaheuristic. The simulation system is named “Stochastic Inventory System Simulator” (SISI) and considers five stochastic inventory models with a wide range of probability distributions for demand and lead time. We report in this paper the computational experience obtained with some numerical examples and a comparison between the solutions obtained with SISI and the ones reported by using other methods.