Francesca Guerriero | University of Calabria (original) (raw)
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Papers by Francesca Guerriero
Computational Optimization and Applications, Jun 1, 2005
Rollout algorithms are innovative methods, recently proposed by Bertsekas et al. [3], for solving... more Rollout algorithms are innovative methods, recently proposed by Bertsekas et al. [3], for solving NP-hard combinatorial optimization problems. The main advantage of these approaches is related to their capability of magnifying the effectiveness of any given heuristic algorithm. However, one of the main limitations of rollout algorithms in solving large-scale problems is represented by their computational complexity. Innovative versions of
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Parallel Computing, May 1, 2003
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Optimization Methods & Software, 2002
Rollout algorithms are new computational approaches used to determine near-optimal solutions for ... more Rollout algorithms are new computational approaches used to determine near-optimal solutions for deterministic and stochastic combinatorial optimization problems. They are built on a generic base heuristic with the aim to construct another hopefully improved heuristic. However, rollout algorithms can be very expensive from the computational point of view, so their use for practical applications can be limited. In this article,
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Dottorato di Ricerca in: Ricerca Operativa, XXIV Ciclo, a.a. 2010-2011
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Lecture Notes in Computer Science, 2019
The world is witnessing an unprecedented growth of needs in data analytics. Big Data is distingui... more The world is witnessing an unprecedented growth of needs in data analytics. Big Data is distinguished by its three main characteristics: velocity, variety and volume. An open issue and challenge faced by the data community is how to scale up analytic algorithms. To address this issue, optimization of large scale data sets has attracted many researchers in recent years. In this paper, we first present the most recent advances in optimization of Big Data analytics. Further, we introduce a fully distributed stochastic optimization algorithm for decision making over large scale data sets. We also propose the optimal weight design for the proposed algorithm and study its performance by considering a practical application in cognitive networks. Experimental results confirm that the proposed method performs well, proven to be distributed, scalable and robust to missing data and communication failures.
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IEEE Transactions on Mobile Computing
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Proceedings of the 7th International Conference on Operations Research and Enterprise Systems
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In this paper we analyze the resource-constrained project scheduling problem under uncertainty. P... more In this paper we analyze the resource-constrained project scheduling problem under uncertainty. Project activities are assumed to have known deterministic renewable resource requirements and probabilistic activity durations described by random variables with a given density function. We develop heuristic algorithms for building a schedule with protected starting times, obtained using a buffering mechanism guided by probabilistic information.
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Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, 2019
Unmanned Aerial Vehicles (UAVs) represent an important asset in the increasing automation require... more Unmanned Aerial Vehicles (UAVs) represent an important asset in the increasing automation required in Smart Cities. However, their short flight autonomy and limited computational resources raise a set of issues to unravel for their full exploitation. In this paper, some of these issues are addressed by leveraging the deployment of Training and Recharging Areas (TRA) in the Smart City. While moving among different Mission Areas (MA), the UAVs pass through dedicated TRA, where Energy and Data Dispensers (EDD) are scattered to allow UAV battery recharge and software update. The tridimensional trajectory planning problem to let the UAVs efficiently and swiftly move through the EDDs, towards the exit of the TRA, is investigated. A mathematical formulation of the problem is presented, and an online approach is designed to achieve a feasible implementation in a realistic scenario.
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Dottorato di Ricerca in: Ricerca Operativa, XXIV Ciclo, a.a. 2008-2011
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Procedia Manufacturing, 2020
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Transportation Research Part C: Emerging Technologies, 2020
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IEEE Transactions on Mobile Computing, 2019
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Computers & Operations Research, 2018
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International Journal of Productivity and Quality Management, 2016
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Ad Hoc Networks, 2017
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Computational Optimization and Applications, Jun 1, 2005
Rollout algorithms are innovative methods, recently proposed by Bertsekas et al. [3], for solving... more Rollout algorithms are innovative methods, recently proposed by Bertsekas et al. [3], for solving NP-hard combinatorial optimization problems. The main advantage of these approaches is related to their capability of magnifying the effectiveness of any given heuristic algorithm. However, one of the main limitations of rollout algorithms in solving large-scale problems is represented by their computational complexity. Innovative versions of
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Parallel Computing, May 1, 2003
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Optimization Methods & Software, 2002
Rollout algorithms are new computational approaches used to determine near-optimal solutions for ... more Rollout algorithms are new computational approaches used to determine near-optimal solutions for deterministic and stochastic combinatorial optimization problems. They are built on a generic base heuristic with the aim to construct another hopefully improved heuristic. However, rollout algorithms can be very expensive from the computational point of view, so their use for practical applications can be limited. In this article,
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Dottorato di Ricerca in: Ricerca Operativa, XXIV Ciclo, a.a. 2010-2011
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Lecture Notes in Computer Science, 2019
The world is witnessing an unprecedented growth of needs in data analytics. Big Data is distingui... more The world is witnessing an unprecedented growth of needs in data analytics. Big Data is distinguished by its three main characteristics: velocity, variety and volume. An open issue and challenge faced by the data community is how to scale up analytic algorithms. To address this issue, optimization of large scale data sets has attracted many researchers in recent years. In this paper, we first present the most recent advances in optimization of Big Data analytics. Further, we introduce a fully distributed stochastic optimization algorithm for decision making over large scale data sets. We also propose the optimal weight design for the proposed algorithm and study its performance by considering a practical application in cognitive networks. Experimental results confirm that the proposed method performs well, proven to be distributed, scalable and robust to missing data and communication failures.
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IEEE Transactions on Mobile Computing
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Proceedings of the 7th International Conference on Operations Research and Enterprise Systems
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In this paper we analyze the resource-constrained project scheduling problem under uncertainty. P... more In this paper we analyze the resource-constrained project scheduling problem under uncertainty. Project activities are assumed to have known deterministic renewable resource requirements and probabilistic activity durations described by random variables with a given density function. We develop heuristic algorithms for building a schedule with protected starting times, obtained using a buffering mechanism guided by probabilistic information.
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Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications, 2019
Unmanned Aerial Vehicles (UAVs) represent an important asset in the increasing automation require... more Unmanned Aerial Vehicles (UAVs) represent an important asset in the increasing automation required in Smart Cities. However, their short flight autonomy and limited computational resources raise a set of issues to unravel for their full exploitation. In this paper, some of these issues are addressed by leveraging the deployment of Training and Recharging Areas (TRA) in the Smart City. While moving among different Mission Areas (MA), the UAVs pass through dedicated TRA, where Energy and Data Dispensers (EDD) are scattered to allow UAV battery recharge and software update. The tridimensional trajectory planning problem to let the UAVs efficiently and swiftly move through the EDDs, towards the exit of the TRA, is investigated. A mathematical formulation of the problem is presented, and an online approach is designed to achieve a feasible implementation in a realistic scenario.
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Dottorato di Ricerca in: Ricerca Operativa, XXIV Ciclo, a.a. 2008-2011
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Procedia Manufacturing, 2020
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Transportation Research Part C: Emerging Technologies, 2020
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IEEE Transactions on Mobile Computing, 2019
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Computers & Operations Research, 2018
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International Journal of Productivity and Quality Management, 2016
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Ad Hoc Networks, 2017
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Proceedings of Third …
In this paper we propose some “innovative” optimization models for Wireless Sensor Networks. The ... more In this paper we propose some “innovative” optimization models for Wireless Sensor Networks. The models are chosen depending on the task the network is called to execute and they focus on the optimization of some specific performance objectives. Indeed, starting from a generic configuration, the optimal solution defines a specific sensors displacement, which allows the network to achieve high performance, in terms of energy consumption and travelled distance. Controlled mobility of the nodes is used for reaching the wanted displacement. The behaviour of the proposed models has been evaluated in comparison with a distributed algorithm on the basis of an extensive computational study and by considering different scenarios.
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