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This paper presents a methodology based on genetic Algorithms (GA) to solve the problem of optimal participation in multiple electricity markets. With the emergence of new requirements for electrical power markets, it has become fundamental to develop tools to aid in decision making, understanding the functioning of markets and forecast iterations that occur between the different entities in the market. Artificial intelligence plays a crucial role in the development of these tools. Using artificial intelligence techniques, it is possible to simulate the different existing players in the market, to enable these players to be adaptive to any situation, and to model any type of trading. Artificial intelligence based metaheuristic optimization tools allow solving problems in a short time, and with very close results to those that deterministic techniques are able to achieve, at the cost of a high execution time. The achieved results, using a simulation scenario based on real data from the Iberian electricity market, show that the proposed method is able to reach better results than previous implementations of a Particle Swarm Optimization (PSO) and a Simulated Annealing (SA) methods, while achieving very similar objective function results to those of a deterministic approach, in a much faster execution time.

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This Spring Issue will discuss about big data and multiple aspects of its usability and applicability. Many of us have seen blockbuster movies Back to the future (premiere in 1985), The Terminator (1984) or Minority report (2002). The unifying element of the above mentioned movies is that manuscripts are introducing a superior competitive advantage factor. The protagonists create an advantage by having either real-time data (sometimes from the future) or all relevant (big and historical) data with enormous computing capacity over competitors. A bit after first two of those movies premiered, NASA scientists Cox and Ellsworth (1997) published an article where term ‘big data’ appeared first time (Press, 2014). Intelligence needs to be topped up in a way to create advantage. Data has been there for a long time, in all forms and sizes. It is applied in almost single every business sector and it is getting faster in sense of usability. The data storage capacity has been exponentially increasing over time, but the usability of this wealth of data remains a critical issue.(...)

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This article intends to debate the institutional modes of first-year adjustment to higher education. Specifically, the aim is to analyze and consider the need to include social and academic integration activities in the curricular programs. The presented contributions are based on the investigations over the case study course that was studied using non-experimental and descriptive approach. The “Projeto FEUP” (Faculty of Engineering of the University of Porto) course is analyzed as it addresses the mentioned integration concerns explicitly by providing tutoring by selected older students (relating to social integration) and adequate teamwork challenges (relating to academic integration). The presented case study course is given to about 1,000 students across nine engineering degrees at the Faculty of Engineering of University of Porto, in Portugal. The article includes some details about not infrequent practices such as “hazing” of newcomer students and strategies for circumventing associated disadvantages. The results shown in the article indicate that the students involved tend to see it as a significant academic integration device, regarding mainly academic work and expectations. © The Author(s) 2015.

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Complexity in manufacturing systems appears under a variety of aspects, namely product, processes and operations and systems. Considering that the manufacturing environment is rapidly and constantly changing, with higher levels of customization and complexity, there is higher demand for flexibility and adaptability from companies. In this context, it seems essential to explore new approaches that can support decision-makers to take better decisions concerning the action plans that they need to launch to achieve the expected strategic and operational performance and alignment goals. Companies should become able to analyse their performance drivers, understand their meaning and the feedback loops that affect them. Therefore, decision makers can look into the future, and act even before these causes affect the transformation systems efficiency and effectiveness. This paper presents an approach oriented to multi-performance measurement in complex manufacturing environments. With this approach it is expected to overcome the gap between the operational and strategic layers of a manufacturing system, in order to reduce time when measuring performance and reacting to unexpected behaviours, as well as reduce errors when taking decisions. Moreover, it is expected to decrease the time necessary to calculate an indicator or to introduce a new one into performance management process, reducing the operational costs.