Abstract
The process of learning is emphasised in all organisations as a way to improve performance, to more effectively manage projects, processes, and activities, and thus, it is of extreme importance to the business in general, because it enables sustainable competitive advantage. However, organisations do not seem to learn from their mistakes, rarely exploring the reasons for their projects' success or failure, and very rarely applying those lessons learned to the business management. In fact, there is little or no point in learning unless management adapts its behavior accordingly. Our proposal is aimed at creating an organisational framework for managing the knowledge produced, as well as incorporating the lessons learned in the daily organisational routines. The purpose is to better respond to the problems that may occur and therefore, strategically realign the organisation towards efficiency and results improvement. © 2012, IGI Global.

Abstract
In the present paper, management strategies are analysed in order to evaluate the degree of entrepreneurship in firms' management by the use of innovation, risk and proactivity strategies. Since we are dealing with management strategies, it is possible to relate them to the concept of Intrapreneurship. This study was done in a region of northern (Portugal Vale do Sousa) and focus on Industrial and Construction sectors. The region is composed of six concelhos1 in some of which it is possible to identify some industrial districts. In order to get a valid sample, a group of 251 firms were analysed. Each strategy was analysed individually and the results pointed to a lack of culture of entrepreneurship in firms' management. Only Proactivity presented a positive result in firms' management. When grouping the results, it was possible to conclude that the degree of intrapreneurship is very low and firms are surviving (even succeeding) by following conventional (old fashioned) management strategies.

Abstract
A virtual enterprise (VE) is a temporary organisation that pools member enterprises core competencies and exploits fast changing market opportunities. VEs offer new opportunities to companies operating with a growing number of participants (consumers, vendors, partners and others) in a global business environment. The success of such an organisation is strongly dependent on its composition, and the selection of partners therefore becomes a crucial issue. Partner selection can be viewed as a multi-criteria decision making problem that involves assessing trade-offs between conflicting tangible and intangible criteria, and stating preferences based on incomplete or non-available information. In general, this is a very complex problem due to the large number of alternatives and criteria of different types (quantitative, qualitative and stochastic). In this paper we propose an integrated approach to rank alternative VE configurations using an extension of TOPSIS (a technique for ordering preferences by similarity to an ideal solution) for fuzzy data, improved through the use of a tabu search meta-heuristic. A sensitivity analysis is also presented. Preliminary computational results clearly demonstrate the potential of the approach for practical application.

Abstract
We propose a multiobjective local search metaheuristic for a mean-risk multistage capacity investment problem with irreversibility, lumpiness and economies of scale in capacity costs. Conditional value-at-risk is considered as a risk measure. Results of a computational study are presented and indicate that the approach is capable of producing high-quality approximations to the efficient sets with a modest computational effort. The best results are achieved with a new hybrid approach, combining Tabu Search and Variable Neighbourhood Search.

Abstract
In this paper we address two major challenges presented by stochastic discrete optimisation problems: the multiobjective nature of the problems, once risk aversion is incorporated, and the frequent difficulties in computing exactly, or even approximately, the objective function. The latter has often been handled with methods involving sample average approximation, where a random sample is generated so that population parameters may be estimated from sample statistics-usually the expected value is estimated from the sample average. We propose the use of multiobjective metaheuristics to deal with these difficulties, and apply a multiobjective local search metaheuristic to both exact and sample approximation versions of a mean-risk static stochastic knapsack problem. Variance and conditional value-at-risk are considered as risk measures. Results of a computational study are presented, that indicate the approach is capable of producing high-quality approximations to the efficient sets, with a modest computational effort.

Abstract
Automated Guided Vehicles (AGV) are self-driven vehicles used to transport material between workstations in the shop floor without the help of an operator, although they can also be applied in security and exploration. They are widely used in material handling systems and flexible manufacturing systems, where production orders are constantly changing. Today, and due to the constant development of technology, sophisticated machinery is increasingly available, thus enabling manufacturing firms to achieve significant process and setup time reductions. With this development, enterprises are encouraged to leave mass production approaches and start adopting small productions lot sizes, leading to constant changes in the production operation's sequences as well as changes in the factory layout. As a consequence of the development of technology, products started to spend a big percentage of time in the queue line or being transported from one workstation/storage to another. With the introduction of AGVs production process flexibility may increase, which, in many productions processes, is still below the expectations due to the used transportation system (ex: conveyors). At the same time, with the AGVs it is possible, to decrease transportations times and costs. In this article, we will study by means of simulation, the impact of the use of an AGV transportation based system in an industrial coating application. The AGV will be responsible for transporting the parts from the system's entrance to the workstations. With this, flexibility in the production process will increase, which will be reflected in system's productivity. © 2010 IFAC.