Abstract
The increasing globalization of the past decades forces the companies to update its supply network strategy in line with the new and challenging context of a competitive market and additional uncertainty. This research, through the development of a model, wants to shed light on which are the most important determinants that influence the supply chain configuration and strategy and how they impact on the performance of the whole supply chain. © 2012 IEEE.

Abstract
The environmental concerns are having a significant impact on the operation of power systems. The traditional Unit Commitment problem, which to minimizes the fuel cost is inadequate when environmental emissions are also considered in the operation of power plants. This paper presents a Biased Random Key Genetic Algorithm (BRKGA) approach combined with non-dominated sorting procedure to find solutions for the unit commitment multiobjective optimization problem. In the first stage, the BRKGA solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval [0,1]. In the subsequent stage, a non-dominated sorting procedure similar to NSGA II is employed to approximate the set of Pareto solution through an evolutionary optimization process. The GA proposed is a variant of the random key genetic algorithm, since bias is introduced in the parent selection procedure, as well as, in the crossover strategy. Test results with the existent benchmark systems of 10 units and 24 hours scheduling horizon are presented. The comparison of the obtained results with those of other Unit Commitment (UC) multiobjective optimization methods reveal the effectiveness of the proposed method.

Abstract

Abstract
Purpose - The purpose of this paper is to study the alignment measurement in collaborative networks, using the fit concept and predictive performance measurement as its main enablers. A performance prediction approach is used in order to control a collaborative business network based not only in present and past performance measurements of each partner, but also taking into account the future behaviour of the intra- and inter-organisational processes performance. Design/methodology/approach - An exploratory case study was applied to a Brazilian collaborative network and mathematical approaches normally used in control theory were adopted to support alignment measurement. Findings - The use of predictive measurements to manage the alignment between the results of inter-organisational processes and performance targets set by the collaborative network. Research limitations/implications - This approach was applied in a specific supply chain network, based on three industrial companies. For other network typologies it will be necessary to evaluate the alignment that can be achieved. Practical implications - This predictive approach makes it possible to manage performance pro-actively using feedforward and feedback control. Therefore, tools that consider performance estimation are used based on a data fusion approach, with a proper combination of leading and lagging measurements, which make it possible to use forecasting methods and tools to achieve good predictions. Originality/value - The paper introduces an approach to alignment measurement leveraged by the new paradigm of performance prediction and presents an alignment metric for collaborative networks. © Emerald Group Publishing Limited.

Abstract
In this paper the Wiener-Hammerstein Benchmark is identified as a bilinear discrete system. The bilinear approximation relies on both facts that the Wiener-Hammerstein system can be described by a Volterra series which can be approximated by bilinear systems. The identification is performed with an iterative bilinear subspace identification algorithm previously proposed by the authors. In order to increase accuracy, polynomial static nonlinearities were added to the bilinear model input. These Hammerstein type bilinear models are then identified using the same iterative subspace identification algorithm.

Abstract
The usage of data mining models has the main purpose of discovering new patterns from dataset analysis by extracting knowledge from data and converting it to information. The most challenging part of problem solving is not the generation of high number of instances in dataset, most often hard to understand, but the interpretation of all those instances to extrapolate information about it. Simulation of coastal ecosystems is used to replicate some real conditions related with physical, chemical and biological processes, and produces large datasets from which it could be deduced some information about attributes behaviors. This paper relates the use of Decision Tree models to analyze the growth of bivalve species in an ecosystem simulation. With a set of attributes that represents the water quality in certain modeled regions, the usage of Decision Tree is intended to identify the most significant attribute conditions, which could justify the growth behavior for each analyzed species. This approach aims the creation of new information about how water conditions should be to promote a healthy and fast growth of the analyzed species, being useful to know in which zones the bivalve should be seeded, and which are the conditions that aquaculture producers should afford to benefit the quality of its crops.