GA-Based Solutions Comparison for Storage Strategies Optimization for an Automated Warehouse

The paper analyses the issues behind strategies optimization of an existing automated warehouse for the steelmaking industry. Genetic algorithms are employed to this purpose by deriving a custom chromosome structure as well as ad-hoc crossover and mutation operators. A comparison between three different solutions able to deal with multiobjective optimization are presented: the first approach is based on a common linear weighting function that combines different objectives; in the second, a fuzzy system is used to aggregate objective functions, while in the last the strength Pareto genetic algorithm is applied in order to exploit a real multiobjective optimization. These three approaches are described and results are presented in order to highlight benefits and pitfalls of each technique.