Optimizing Complex Multi-location Inventory Models Using Particle Swarm Optimization

The efficient control of logistics systems is a complicated task. Analytical models allow to estimate the effect of certain policies. However, they necessitate the introduction of simplifying assumptions, and therefore, their scope is limited. To surmount these restrictions, we use Simulation Optimization by coupling a simulator that evaluates the performance of the system with an optimizer. This idea is illustrated for a very general class of multi-location inventory models with lateral transshipments. We discuss the characteristics of such models and introduce Particle Swarm Optimization for their optimization. Experimental studies show the applicability of this approach.

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