GA-based solutions comparison for warehouse storage optimization

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

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