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Università della Svizzera italiana

Efficient combinatorial optimization algorithms for logistic problems

Papapanagiotou, Vasileios ; Gambardella, Luca Maria (Dir.) ; Montemanni, Roberto (Codir.) ; Schmidhuber, Jürgen (Codir.)

Thèse de doctorat : Università della Svizzera italiana, 2018 ; 2018INFO011.

The field of logistics and combinatorial optimization features a wealth of NP-hard problems that are of great practical importance. For this reason it is important that we have efficient algorithms to provide optimal or near-optimal solutions. In this work, we study, compare and develop Sampling-Based Metaheuristics and Exact Methods for logistic problems that are important for their...

Università della Svizzera italiana

Self-adaptivity of applications on network on chip multiprocessors : the case of fault-tolerant Kahn process networks

Derin, Onur ; Sami, Mariagiovanna (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2015 ; 2015INFO010.

Technology scaling accompanied with higher operating frequencies and the ability to integrate more functionality in the same chip has been the driving force behind delivering higher performance computing systems at lower costs. Embedded computing systems, which have been riding the same wave of success, have evolved into complex architectures encompassing a high number of cores interconnected...

Università della Svizzera italiana

Stochastic vehicle routing : from theory to practice

Weyland, Dennis ; Gambardella, Luca Maria (Dir.) ; Montemanni, Roberto (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2013 ; 2013INFO002.

In this thesis we discuss practical and theoretical aspects of various stochastic vehicle routing problems. These are combinatorial optimization problems related to the field of transportation and logistics in which input data is (partially) represented in a stochastic way. More in detail, we focus on two-stage stochastic vehicle routing problems and in particular on so-called a priori...

Università della Svizzera italiana

Teaching networks how to learn : reinforcement learning for data dissemination in wireless sensor networks

Förster, Anna ; Murphy, Amy L. (Dir.)

Thèse de doctorat : Università della Svizzera italiana, 2009 ; 2009INFO001.

Wireless sensor networks (WSNs) are a fast developing research area with many new exciting applications arising, ranging from micro climate and environmental monitoring through health and structural monitoring to interplanetary communications. At the same time researchers have invested a lot of time and effort into developing high performance energy efficient and reliable communication protocols...