Gianluca Nastasi

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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(More)
In this paper a novel ensemble method (EM) for classification tasks is described. The proposed approach is based on the use of a set of classifiers, each of which is trained by exploiting a different subset of the available training data, which are created by partitioning the input space by means of a self organizing map (SOM) based clustering algorithm.(More)
An extremely important part of the finishing line is the pickling process, in which oxides formed during the hot rolling stage are removed from the surface of the steel sheets. The efficiency of the pickling process is mainly dependent on the nature of the oxide present at the surface of the steel, but, also, on process parameters such as bath composition(More)
The paper presents a software which simulates an existing automated warehouse of steel tubes, including all the movements of the tubes packs conveyors as well as the management of all the operations for their storage and recovery for delivery to customers. The reason to develop such simulator lies in the needing to test new stocking strategies, that could(More)
Distribution fitting is a widely recurring problem in different fields such as telecommunication, finance and economics, sociology, physics, etc. Standard methods often require solving difficult equations systems or investments in specialized software. The paper presents a new approach to distribution fitting that exploits Genetic Algorithms in order to(More)