Abstract
Manufacturing companies are being pushed to their limits due to an increase of production complexity guided by a growing standards demand by the costumers. To respond properly to this, manufacturing companies must adopt innovative control architectures that are able to handle better the occurrence of disturbances at shop-floor level (e.g. workstation breakdown, orders cancellation or modification). Additionally, the selection of a proper scheduling algorithms assumes a crucial point, in the sense that the increase of optimization levels depend on this. This paper presents a Genetic Algorithm (GA) based technique to be embedded into the supervisor entity present at the ADACOR(2) aiming to improve the existing fast and non-optimal scheduling technique, improving the overall system processing execution. The main requirements of the GA is to be fast enough to be usable in demanding environments improving the optimization output. The proposed algorithm is tested using a Flexible Manufacturing System using different configurations of transportation and batch sizes. Results show that despite the presented GA technique increased the optimization calculation time it performs better considering the sum of this time with the gain in the optimization output.

Abstract
Ambient intelligence (AmI) paradigm has been the catalyst for the creation of several types of smart spaces, and the smart meeting rooms (SMR) is one of them. One of the main aims of AmI spaces is the development of middleware able to provide context aware computing specifically in SMR face-to-face, and distributed/asynchronous meeting settings are envisaged. This paper describes OLAVAmIa context aware middleware system which was tested in LAID environment, a smart meeting room. OLAVAmI allows video production focusing on the speaker, an audio-to-text conversion service, and a multimedia database of meetings produced in an autonomous way. To experiment OLAVAmI usage and functionalities, two tools present in LAID test bed were used. These AmI intelligent layer tools cover two application scenarios: a ubiquitous group decision-making and a service reposition in power systems. The results and insights are presented in this article.

Abstract

Abstract
PVSio-web is a graphical environment for facilitating the design and evaluation of interactive (human-computer) systems. Using PVSio-web, one can generate and evaluate realistic interactive prototypes from formal models. PVSio-web has been successfully used over the last two years for analyzing commercial, safety-critical medical devices. It has been used to create training material for device developers and device users. It has also been used for medical device design, by both formal methods experts and non-technical end users. This paper presents the latest release of PVSio-web 2.0, which will be part of the next PVS distribution. The new tool architecture is discussed, and the rationale behind its design choices are presented. © Springer International Publishing Switzerland 2015.

Abstract
In a multienergy system, there are different types of dependencies among the energy carriers. Internal dependencies refer to possible changes in the energy source in the presence of energy converters and storage, and are managed by the system operator through the control strategies applied to the equipment. External dependencies (EDs) are due to the choice of the energy supply according to customer preferences when alternative solutions are available. This paper introduces a new model of EDs within a multigeneration representation based on energy hubs. EDs are addressed through a stochastic model in order to take into account the possible uncertainty in the customers' decisions. This model is then used to introduce carrier-based demand response (DR) in which the user participates in DR programs aimed at promoting the shifting among different energy sources by preserving the service provided to the end users. The results obtained from the new model in deterministic and stochastic cases indicate the appropriateness and usefulness of the proposed approach.

Abstract
Metaheuristics are very useful to achieve good solutions in reasonable execution times. Sometimes they even obtain optimal solutions. However, to achieve near-optimal solutions, the appropriate tuning of parameters is required. This paper presents a Racing based learning module proposal for an autonomous parameter tuning of Metaheuristics. After a literature review on Metaheuristics parameter tuning and Racing approaches, the learning module is presented. A computational study for the resolution of the Scheduling problem is also presented. Comparing the preliminary obtained results with previous published results allow to conclude about the effectiveness and efficiency of this proposal. © 2015 AISTI.