Optimization of an automated storage and retrieval systems by swarm intelligence

Abstract

Automated storage and retrieval systems (AS/RS) need to execute complex combinatorial and sorting tasks. In this study we have shown how to plan AS/RS using multiple objective ant colony optimisation (ACO). The distribution of products in the AS/RS is based on the factor of inquiry (FOI), product height (PH), storage space usage (SSU) and path to dispatch (PD). The factor of inquiry for any product can be adjusted during the storage process in regard to actual market requirements. In order to reduce space consumption and minimise investment costs we chose an AS/RS with no corridors and one single elevator for multiple products. Results show that the expected distribution of products was reached and that ACO can be successfully used for planning automated storage systems.