Abstract

Abstract
Purpose: The purpose of this paper is to highlight possible hidden risks when allocating multi-skilled human resources to teams working in a multi-project environment. Are allocation strategies maximizing the use of skills for each project, the only way to improve the chances of all projects being successful? What are the risks in this strategy? What are the available alternatives? Design/methodology/approach: Simulation was used for different allocation strategies to evaluate, using two different metrics, the staffing of human resources in different projects. Three categories of companies were studied, and for each typology, virtual companies were created and several scenarios of collaborators, projects and tasks were simulated to evaluate the staffing process. Findings: It is shown that for different simulations, different allocation strategies and metrics are possible for evaluation and that there is no golden rule of staffing in organizations with multiple projects and with multiple skills collaborators. The staffing is very much dependent on the context of the company. Practical implications: The numerical method provides general managers with a useful tool to enable a better distribution of staff collaborators in teams handling multiple projects that require multi-skilled human resources. This method can also be used to evaluate training needs and hiring strategies, as it presents an overview of all human resources skills and motivations. Originality/value: For academics, the methodology developed enables the study of characteristics of human resources, skills and motivations, which are interesting for team formation. To practitioners, the numerical method is a practical tool for staffing in multiple skills and multiple projects. This tool can also diagnose each company situation regarding current collaborators’ skills and motivations, serving as a tool for training and for hiring.

Abstract
In this paper, an optimal scheduling of thermal and wind power plants is presented by using a stochastic programming approach to cover the uncertainties of the forecasted generation of wind farms. Uncertainties related to wind forecast error, consequently wind generation outage power and also system load demand are modeled through scenario generation. Then, with regard to day-ahead and real-time energy markets and taking into account the relevant constraints, the thermal unit commitment problem is solved considering wind energy injection into the system. Besides, in order to assess impacts of Demand Response (DR) on the problem, a load reduction demand response model has been applied in the base model. In this approach, self and cross elasticity is used for modeling the customers' behavior modeling. The results indicate that the DR Programs (DRPs) improves the market efficiency especially in peak hours when the thermal Gencos become critical suppliers and the combination of DRPs and wind farm can be so efficient.

Abstract
In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of several new proposed stream algorithms, ensembles play an important role, in particular for 'non-stationary environments. This paper surveys research on ensembles for data stream classification as well as regression tasks. Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semi supervised learning, complex data representations and structured outputs. The paper concludes with a discussion of open research problems and lines of future research. Published by Elsevier B.V.

Abstract
In the last decades power systems witnessed the implementation of an organizational and operational restructuring that lead to the introduction of competitive mechanisms in some activities of the value chain. This is the case of generation and retailing with the development of wholesale and retail markets. These developments together with a renewed emphasis on the adoption of more sustainable solutions while maintaining adequate security of supply levels contributed to increase the interest of generation companies for models enabling the optimization of the use of generation assets or for models and tools to help them to prepare and test bidding strategies to the day-ahead markets. Having in mind the increased complexity of the operation of power systems, Agent-Based Models, ABM, are been used to complement the traditional optimization and equilibrium models, taking advantage of the interaction between agents acting in a simulation environment. In this scope, this paper describes an ABM model that uses Q-learning to provide knowledge for the agents to behave in an optimal way. This model is designed to mimic the main features of the common electricity market between Portugal and Spain, the MIBEL. Apart from describing the developed model, this paper also includes preliminary results from its application to the MIBEL case.

Abstract
The chapter presents an entrepreneurial perspective to rural tourism. It is based on the utilization of endogenous resources that exist within a rural region, and leads to a group of business opportunities related to tourism, craftwork, and agriculture, which are taken into account to define the strategic objectives for the ADRIMAG region. The chapter follows a qualitative approach to business opportunities. Through our analysis, it was possible to create, in a simple manner, a group of business opportunities based on the endogenous resources of the region. With this study, we expect to bring forth an entrepreneurial perspective that will sustainably foster tourism development within rural regions, but with high potential for tourism attraction.