Developing a New Algorithm for Finding the Local Minimums of the Multi-Echelon Inventory Control Systems with Random Parameters

Abstract

The present study aimed to develop a new simulation-based algorithm for finding the local minimums of multi-level inventory control systems with random parameters. The optimization refers to minimization of cost function along with maximization of customer service level of the units. In developing the algorithm, the authors were determined to achieve a local optimum point through linear localization of limitations and Genetic Algorithm. Since point estimations of goal function and repletion rates have been done through Monte Carlo Simulation Technique, the statistical test have been employed for examining possibility and improvability of solutions. Finally, the proposed algorithm has been used in an example with three levels.