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In today's dynamic market numerous dynamic influencing factors have seriously aggravates the difficulties of inventory planning of the complex distribution networks. This paper proposes a simulation-based hybrid approach for the optimization of the inventory policies in complex distribution networks. The initial multi-echelon inventory policies are handed over to a simulation model, which is capable of modelling complexity and uncertainties of the distribution network and simulating them under respective scenarios. Through comprehensively analysing the KPls (logistic service level and logistic costs) of this set of multi-echelon inventory policies, their levels of robustness can be clearly ascertained. Based on the simulation results, a metaheuristic-based optimizer regenerates improved multi-echelon inventory policies, which are once again comprehensively and precisely evluated through simulation. This closed feedback loop forms a simulation optimization pocess that enables autonomous evolution of multi•echelon inventory policies of complex distribution networks. Since the simulation results can truly reflect the performance of certain inventory policies in real market environment, the new Simulation-Based Hybrid Approach is quite useful for decision making process

Simulation-Based Hybrid Approach to Robust Multi-Echelon Inventory Policies for Complex Distribution Networks