A simulation- and metaheuristic-based approach for inventory optimization of complex distribution systems
Abstract
In today's dynamic market numerous dynamic influencing factors have seriously aggravates the difficulties of inventory planning of the complex distribution systems. This paper proposes a simulation- and metaheuristic-based approach for the optimization of the inventory policies in complex distribution systems. The initial multi-echelon inventory policies are handed over to a simulation model, which is capable of modeling complexity and uncertainties of the distribution network and simulating them under respective scenarios. Through comprehensively analyzing the KPIs (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 (more robust) multi-echelon inventory policies, which are once again comprehensively and precisely evaluated through simulation. This closed feedback loop forms a simulation optimization process that enables the autonomous evolution of multi-echelon inventory policies of complex distribution systems.
