Models for the prediction and management of complex systems in industrial and dynamic environments

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@PhdThesis{Alsina:thesis,

• author = "Emanuel Federico Alsina",
• title = "Models for the prediction and management of complex systems in industrial and dynamic environments",
• title_it = "Modelli per la previsione e la gestione di sistemi complessi in ambienti dinamici e industriali",
• school = "Universita degli studi di Modena e Reggio Emilia",
• year = "2016",
• address = "Italy",
• keywords = "genetic algorithms, genetic programming",
• URN = "etd-11262015-110057",
• URL = "https://morethesis.unimore.it/theses/available/etd-11262015-110057/",
• URL = "https://morethesis.unimore.it/theses/available/etd-11262015-110057/unrestricted/thesis.pdf",
• size = "137 pages",
• abstract = "The world in which we live is becoming more and more complex. Modelling the reality means to create simplifications and abstractions of that, in order to figure out what is going on in this modern and complex world in which we live. Nowadays, models have become crucial to make better decisions. Models help us to be clearer thinkers, and to understand how to transform data in useful information. There are too many data out there, models take these data and structure them into information, and then into knowledge. Two main topics are discussed in this work: (1) how to model complex systems, and (2) how to make predictions within complex systems, in industrial and dynamic environments. The purpose of this thesis is to present a series of models developed to support the decision makers in the complexity management. The first topic is addressed presenting some models concerning the balancing of assembly lines, machine degradation in production lines, operation schedule, and the positing of cranes in automated warehousing. In particular, concerning the assembly lines, two bio-inspired models which optimize the global picking time of the components considering their physical allocation are presented. Moreover, the use of a multi-agent model able to simultaneously consider different factors that affect machines in a production line is analysed. This approach takes into account the ageing and the degradation of the machines, the repairs, the replacement, and the preventive maintenance activities. Furthermore, in order to present how to manage the complexity intrinsic into the operations scheduling, a model inspired by the behaviour of an ant colony is showed. Finally, another multi-agent model is showed, which is able to find the optimal dwell point in automated storage retrieval systems exploiting an idea deriving from force-fields. After that, an entire chapter is dedicated to the prediction in complex systems. Prediction in industrial and dynamic environments is a challenge that professionals and academics have to face more and more. Some models able to capture non-linear relationships between temporal events are presented. These models are applied to different fields, from the reliability of mechanical and electrical components, to renewable energy. In the final analysis, models able to predict the users behaviors within online social communities are introduced. In these cases, various machine learning approaches (such as artificial neural networks, logistic regressions, and random trees) are detailed. This thesis want to be an inspiration for those people which have to manage the complexity in industrial and dynamic environments, showing examples and results, in order to explain how to make this world a little more understandable.",
• notes = "Supervisor: Giacomo Cabri",

}

Genetic Programming entries for Emanuel Federico Alsina

Citations