Abstract
The resources systems selection is still a difficult matter to solve in the Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. Globally, we can say that the resources selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project). We intend in this paper to show: a formulation of a kind of resources selection problem; the performance (computation processing time) of an exact solution algorithm proposed to solve it, according to the number of processing tasks and the number of pre-selected resources per processing tasks; and, as a consequence of the domain of applicability of that algorithm, to propose the formulation an approximate solution algorithm, namely genetic algorithm (GA) based. The final goal it is to improve the knowledge about the algorithms domain for the broker tool, in order to select the most adequate algorithm to perform the resources system selection.

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This chapter presents the innovative contribution for organization performance management in collaborative networks (CNs), developed and validated in the Net-Challenge European RTD project. The chapter first presents the context for strategy formulation and decision in CN and the type of strategic decision faced. Then, the chapter describes the Net-Challenge framework for performance management, starting with an overview, followed by the key concepts of key stakeholder and key success factor (KSF) and closing with the reference process. A preliminary application of this framework in a demonstrator CN in the garment industry showed the applicability of the methodology and usefulness of the process resources, particularly of the scenario templates, to simplify the setup steps of performance management process. © 2013 by John Wiley & Sons, Inc.

Abstract
Heart Rate Variability (HRV) series exhibit long memory and time-varying conditional variance. This work considers the Fractionally Integrated AutoRegressive Moving Average (ARFIMA) models with Generalized AutoRegressive Conditional Heteroscedastic (GARCH) errors. ARFIMA-GARCH models may be used to capture and remove long memory and estimate the conditional volatility in 24 h HRV recordings. The ARFIMA-GARCH approach is applied to fifteen long term HRV series available at Physionet, leading to the discrimination among normal individuals, heart failure patients, and patients with atrial fibrillation. (C) 2013 AIP Publishing LLC

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This paper presents a mathematical model to simultaneously optimize the cost of electricity and the satisfaction of a residential consumer using the communication infrastructure of a smart grid. For this task the concept of Pareto optimality has been used. It is possible to consider the satisfaction of the consumer as an independent objective to be maximized, and simultaneously, to minimize the cost of the electrical bill. In future works a multiobjective evolutionary algorithm will be applied along with the mathematical model presented in this paper. © (2013) Trans Tech Publications, Switzerland.

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