Abstract
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

Abstract

Abstract
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.

Abstract

Abstract
In this work we address the Single-Source Uncapacitated Minimum Cost Network Flow Problem with concave cost functions. This problem is NP-Hard, therefore we propose a hybrid heuristic to solve it. Our goal is not only to apply an ant colony optimization (ACO) algorithm to such a problem, but also to provide an insight on the behaviour of the parameters in the performance of the algorithm. The performance of the ACO algorithm is improved with the hybridization of a local search (LS) procedure. The core ACO procedure is used to mainly deal with the exploration of the search space, while the LS is incorporated to further cope with the exploitation of the best solutions found. The method we have developed has proven to be very efficient while solving both small and large size problem instances. The problems we have used to test the algorithm were previously solved by other authors using other population based heuristics. Our algorithm was able to improve upon some of their results in terms of solution quality, proving that the HACO algorithm is a very good alternative approach to solve these problems. In addition, our algorithm is substantially faster at achieving these improved solutions. Furthermore, the magnitude of the reduction of the computational requirements grows with problem size.