A multi-objective optimization model for minimizing investment expenses, cycle times and co2 footprint of an automated storage and retrieval systems

Abstract

A new optimization model of Automated Storage and Retrieval Systems (AS/RS) containing three objective and four constraint functions is presented in this paper. Majority of the researchers and publications in material handling field had performed optimization of different decision variables, but with single objective function only. Most common functions are: minimum travel time, maximum throughput capacity, minimum cost, maximum energy efficiency, etc. To perform the simultaneous optimization of objective functions (minimum: "investment expenses", "cycle times", "CO2 footprint") the Non-dominated Sorting Genetic Algorithm II (NSGA II) was used. The NSGA II is a tool for finding the Pareto optimal solutions on the Pareto line. Determining the performance of the system is the main goal of our model. Since AS/RS are not flexible in terms of layout and organizational changes once the system is up and running, the proposed model could be a very helpful tool for the warehouse planners in the early stages of warehouse design.