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Journal of Intelligent Manufacturing
Erschienen in: Journal of Intelligent Manufacturing 7/2018

27.01.2016

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

verfasst von: Gianluca Nastasi, Valentina Colla, Silvia Cateni, Simone Campigli

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 7/2018

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Abstract

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.
Vorheriger Artikel Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM
Nächster Artikel Engineering design optimization using an improved local search based epsilon differential evolution algorithm

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Metadaten
Titel
Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse
verfasst von
Gianluca Nastasi
Valentina Colla
Silvia Cateni
Simone Campigli
Publikationsdatum
27.01.2016
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 7/2018
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-016-1198-x

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