Search results for: V. Roca

Authors: F. Loffredo, M. Quarto, M. Pugliese, A. Mazzella, F. De Cicco, V. Roca

Abstract:

The aim of this study is to evaluate the efficacy of radon remedial action in a school on the Ischia island, South Italy, affected by indoor radon concentration higher than the value of 500 Bq/m³. This value is the limit imposed by the Italian legislation, to above which corrective actions in schools are necessary. Before the application of remedial action, indoor radon concentrations were measured in 9 rooms of the school. The measurements were performed with LR-115 passive alpha detectors (SSNTDs) and E-Perm. The remedial action was conducted in one of the office affected by high radon concentration using a Radonstop paint applied after the construction of a concrete slab under the floor. The effect of remedial action was the reduction of the concentration of radon of 41% and moreover it has demonstrated to be durable over time. The chosen method is cheap and easy to apply and it could be designed for various types of building. This method can be applied to new and existing buildings that show high dose values.

Keywords: E-Perm, LR 115 detectors, radon remediation, school.

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Authors: Javier Roca, Etienne Pugnaghi, Gaëtan Libert

Abstract:

We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.

Keywords: Intelligent problem encoding, multiobjective decision making, evolutionary computing, RCPSP, resource leveling.

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