Abstract
Proper management of supply chains is fundamental in the overall system performance of forest based activities. Usually, efficient, management techniques a decision support, software, which needs to be able to generate fast and effective outputs from the set of possibilities. In order to do this, it is necessary to provide accurate models representative of the dynamic interactions of systems. Due to forest-based supply chains' nature, event-based models are more suited to describe their behaviours. This work proposes the modelling and simulation of a forest based supply chain, in particular the biomass supply chain, through the SimPy framework. This Python based tool allows the modelling of discrete-event, systems using operations such as events, processes Mid resources. The developed model was used to access the impact of changes in the daily working plan in three situations. First, as a control case, the deterministic behaviour was simulated. As a second approach, a machine delay was introduced and its implications in the plan accomplishment were analysed. Finally, to better address real operating conditions, stochastic; behaviours of processing and driving times were simulated. The obtained results validate the SirriPy simulation environment as a framework for modelling supply chains in general and for the biomass problem in particular.

Abstract
Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention of local optima, and such robustness comes with high computational overhead, though custom digital processor use may reduce this cost. This paper presents a new implementation of an old, and almost forgotten, evolutionary algorithm: the population-based incremental learning method. We show that the structure of this algorithm is well suited to implementation within programmable logic, as compared with contemporary genetic algorithms. Further, the inherent concurrency of our FPGA implementation facilitates the integration and testing of micro-populations. Copyright

Abstract
In robotics, a reliable simulation tool is an important design and test resource because the performance of algorithms is evaluated before being implemented in real mobile robots. The virtual environment makes it possible to conduct extensive experiments in controlled scenarios, without the dependence of a physical platform, in a faster and inexpensive way. Although, simulators should be able to represent all the relevant characteristics that are present in the real environment, like dynamic (shape, mass, surface friction, etc.), impact simulation, realistic noise, among other factors, in order to guarantee the accuracy and reliability of the results. This paper presents a ROS (Robot Operating System) framework for the SimTwo simulator. ROS is an open-source library that is commonly used for the development of robotic applications since it provides standard services and promotes large-scale integrative robotic research. SimTwo is a realistic simulation software suitable for test and design of several types of robots. This simulator conducts realistic navigation procedures, since the driving systems, the sensors, the mechanical and physical properties of the bodies are precisely modeled. The framework presented in this research provides the integration of ROS-based systems with the SimTwo simulator. Therefore, this framework reduces the risk of damage of expensive robotic platforms and it can be used for the development of new mobile robot controllers, as well as for educational purposes.

Abstract
There is a need to attract students to science and engineering courses. Robotic contests are one of the most promising ways to attract students to the field of robotics and thereby to science and technology. Micromouse contest is one of promising contests where a small autonomous robot has to navigate its way through an unknown 16 x 16 cells maze. Since the design and construction of robots is interesting but difficult, this paper presents a high performance, low-cost robot kit for high school and university students participate on micromouse robot contests. This micromouse kit, developed at University of Tras-os-Montes and Alto Douro (UTAD), fits on a 10x8 cm rectangle and uses very small stepper motors allowing a maximum speed of about 4 m/s thus comparing to state-of-the-art micromice. The kit also incorporates a popular microcontroller hardware module (Arduino Leonardo) which facilitates all programming tasks.

Abstract
The organizational struck of the companies in the biomass energy sector, regarding the supply chain management, services, can be greatly improved through the use of software decision support. tools. These tools should be able to provide real-time alternative scenarios when deviations from the initial production plans are observed. To make this possible it is necessary to have representative production chain process models where several scenarios and solutions can be evaluated accurately. Due to its nature, this type of process is more adequately represented by means of event-based models. to particular, this work presents the modelling a typical biomass production chain using the computing platform SIMEVENTS. Throughout the article details about the conceptual model, as well as simulation results, are provided.

Abstract
Aim of study: This work aims to provide an overview of Model Predictive Controllers (MPC) applications in supply chains, to describe the forest-based supply chain and to analyse the potential use and benefits of MPC in a case study concerning a biomass supply chain. Area of study: The proposed methods are being applied to a company located in Finland. Material and methods: Supply chains are complex systems where actions and partners' coordination influence the whole system performance. The increase of competitiveness and need of quick responses to the costumers implies the use of efficient management techniques. The control theory, particularly MPC, has been successfully used as a supply chain management tool. MPC is able to deal with dynamic interactions between the partners and to globally optimize the supply chain performance in the presence of disturbances. However, as far as is authors' knowledge, there are no applications of this methodology in the forest-based supply chains. This work proposes a control architecture to improve the performance of the forest supply chain. The controller is based on prediction models which are able to simulate the system and deal with disturbances. Main results: The preliminary results enable to evaluate the impacts of disturbances in the supply chain. Thus, it is possible to react beforehand, controlling the schedules and tasks' allocation, or alert the planning level in order to generate a new plan. Research highlights: Overview of MPC applications in supply chains; forest-based supply chain description; case study presentation: wood biomass supply chain for energy production; MPC architecture proposal to decrease the operation times.