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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10990/445

Autori: Urli, Tommaso
Supervisore afferente all'Università: DI GASPERO, LUCA
Centro di ricerca: DIPARTIMENTO INGEGNERIA ELETTRICA GESTIONALE MECCANICA - DIEG
Titolo: Hybrid meta-heuristics for combinatorial optimization
Abstract (in inglese): Combinatorial optimization problems arise, in many forms, in vari- ous aspects of everyday life. Nowadays, a lot of services are driven by optimization algorithms, enabling us to make the best use of the available resources while guaranteeing a level of service. Ex- amples of such services are public transportation, goods delivery, university time-tabling, and patient scheduling. Thanks also to the open data movement, a lot of usage data about public and private services is accessible today, sometimes in aggregate form, to everyone. Examples of such data are traffic information (Google), bike sharing systems usage (CitiBike NYC), location services, etc. The availability of all this body of data allows us to better understand how people interacts with these services. However, in order for this information to be useful, it is necessary to develop tools to extract knowledge from it and to drive better decisions. In this context, optimization is a powerful tool, which can be used to improve the way the available resources are used, avoid squandering, and improve the sustainability of services. The fields of meta-heuristics, artificial intelligence, and oper- ations research, have been tackling many of these problems for years, without much interaction. However, in the last few years, such communities have started looking at each other’s advance- ments, in order to develop optimization techniques that are faster, more robust, and easier to maintain. This effort gave birth to the fertile field of hybrid meta-heuristics.
Parole chiave: Meta-heuristics; Hybrid-heuristics; Combinatorial optimization
MIUR : Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni
Lingua: eng
Data: 4-apr-2014
Corso di dottorato: Dottorato di ricerca in Ingegneria industriale e dell'informazione
Ciclo di dottorato: 26
Università di conseguimento titolo: Università degli Studi di Udine
Luogo di discussione: Udine
Citazione: Urli, T. Hybrid meta-heuristics for combinatorial optimization. (Doctoral Thesis, Università degli Studi di Udine, 2014).
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