Gabriella Dellino

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Simulation-optimization aims to identify the setting of the input parameters (input variables, factors) of the simulated system that leads to the optimal system performance. In practice, however, some of these parameters cannot be perfectly controlled — due to measurement errors or other implementation issues, and the inherent uncertainty caused by(More)
Optimization of simulated systems is the goal of many methods, but most methods assume known environments. In this paper we present a methodology that does account for uncertain environments. Our methodology uses Taguchi's view of the uncertain world, but replaces his statistical techniques by either Response Surface Methodology or Kriging metamodeling. We(More)
This paper concerns the problem of optimally scheduling a set of appliances at the end-user premises. The user’s energy fee varies over time, and moreover, in the context of smart grids, the user may receive a reward from an energy aggregator if he/she reduces consumption during certain time intervals. In a household, the problem is to decide when to(More)
Most methods in simulation-optimization assume known environments, whereas this research accounts for uncertain environments combining Taguchi's world view with either regression or Kriging (also called Gaussian Process) metamodels (emulators, response surfaces, surrogates). These metamodels are combined with Non-Linear Mathematical Programming (NLMP) to(More)
This research aims at supporting hospital management in making prompt Operating Room (OR) planning decisions, when either unpredicted events occur or alternative scenarios or configurations need to be rapidly evaluated. We design and test a planning tool enabling managers to efficiently analyse several alternatives to the current OR planning and scheduling.(More)
This paper addresses the problem of optimal management of consumer flexibility in an electric distribution system. Aggregation of a number of consumers clustered according to appropriate criteria, is one of the most promising approaches for modifying the daily load profile at nodes of an electric distribution network. Modifying the daily load profile is(More)
In this work, the ability of the Dynamic Objectives Aggregation Methods to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneously optimization of the risk on portfolio, the expected return(More)
The Optimal Power Flow problem (OPF) plays a crucial role in the successful energy management of modern smart grids. The diffusion of renewable energy sources poses new challenges to the power grid in which integrated energy storage combined with green generation solutions can help to address challenges associated with both power supply and demand(More)