Abstract
In this work we present a tabu search metaheuristic method for solving the permutation flow shop scheduling problem with sequence dependent setup times and the objective of minimising total weighted tardiness. The problem is well known for its practical applications and for the difficulty in obtaining good solutions. The tabu search method proposed is based on the insertion neighbourhood, and is characterised by the selection and evaluation of a small subset of this neighbourhood at each iteration; this has consequences both on diversification and intensification of the search. We also propose a speed-up technique based on book keeping information of the current solution, used for the evaluation of its neighbours. © 2014 Inderscience Enterprises Ltd.

Abstract
Within collaborative networks, information sharing and knowledge creation are the main drives for value creation, wherever collaborative spaces (CS) has been used as the means to enable collaboration among the different social actors involved. Despite of technological availability, CS still a challenge in practice, mainly due to the lack of methods to support its development and to its tight coupling with the collaboration model adopted by the network. Thus, the main focus of this paper in on enhancing information sharing through the design of what we call agnostic collaborative spaces (ACS), supported by linked data approaches. Beyond this new perspective over CS, it is discussed a technological solution through which ACS are implemented.

Abstract
A process of risk management in projects is a rational chain of practices by which decision-agents plan and execute actions and control the results in order to keep the implementation of the project under certain conditions (time, cost and quality parameters' set). With the purpose of providing guidelines for the selection of the best practices taking into account the organizational maturity and project complexity, a theoretical framework to classify and associate those practices to each phase of the project life cycle and to each project risk management process is proposed. Future research efforts will be directed towards refining the framework and testing it in multiple case studies. (C) 2014 The Authors. Published by Elsevier Ltd.

Abstract
The kidney exchange problem (KEP) is an optimization problem arising in the framework of transplant programs that allow exchange of kidneys between two or more incompatible patient-donor pairs. In this paper an approach based on a new decomposition model and branch-and-price is proposed to solve large KEP instances. The optimization problem considers, hierarchically, the maximization of the number of transplants and the minimization of the size of exchange cycles. Computational comparison of different variants of branch-and-price for the standard and the proposed objective functions are presented. The results show the efficiency of the proposed approach for solving large instances.

Abstract
Bonding is an important process used in all fields of industry, where the tight joining of two materials is required. It includes a wide variety of processing technologies that can be placed in a framework of chemistry, physics, and materials science. Although most of these bonding processes have only recently appeared in textbooks, the basic phenomena have been known and used for many centuries. Choosing an appropriate bonding process may result in the improved end-use performance, increased efficiency, and greater design flexibility. Through various bonding techniques, this study aims at investigating the following ones: direct bonding, thermocompression bonding, surface activated bonding, eutectic bonding, adhesive bonding, and glass frit bonding. The characteristic features of these techniques with respect to their many-sided aspects and a review of the current state of the art of each technique are briefly outlined in this chapter.

Abstract
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.