Abstract
The Information Technology Outsourcing (ITO) model has been a trend in recent decades, becoming the dominant trend in contemporary outsourcing scenario. Millennials will soon, globally, be the majority of the workforce, having a particular way of relating to organizations and to work itself as no previous generation did. Information technologies continue to employ more and more human resources, having an increasing demand and a shortage of competent resources. It is therefore important to rethink current Human Resources Management (HRM) models and design a new strategic and appropriate model to accommodate and anticipate the needs of managers and monitor the development of this generation as Information Technology (IT) professionals. The intersection and analysis of the Information and Communications Technologies (ICT), millennials, Human Resource Management, outsourcing and organizations is the objective of this study, to identify the most relevant articles regarding millennials as outsourced IT professionals.

Abstract
Backrooms are an important echelon of the retail supply chain. However, research focus has been mostly targeted to optimise both distribution centres and stores' sales area. In this paper, we propose two mathematical programming formulations to solve the grocery backroom sizing problem. This problem consists of determining the dimension of each storage department in the backroom area to optimise its overall efficiency. The first formulation is a bottom-up approach that aims to reduce the backroom life-cycle costs by determining the optimum floor space and storage height for each department. The second is a top-down approach based on Data Envelopment Analysis (DEA), which determines the efficient level of storage floor space for each backroom department, based on a comparison with the benchmarks observed among existing stores. Each approach has distinct characteristics that turn the models suitable for different retail contexts. We also describe the application of the proposed approaches to a case study of a European retailer. The application of this methodology in the design process demonstrated substantial potential for space savings (6% for the bottom-up model and 16% for the top-down model). This space reduction should either allow higher revenues in the sales area and/or lower backroom-related costs.

Abstract
Integrated transmission expansion planning (TEP) and generation expansion planning (GEP) with Wind Farms (WFs) is addressed in this paper. The optimal number of expanded lines, the optimal capacity of WFs installed capacity, and the optimal capacity of wind farms lines (WFLs) are determined through a new TEP optimization model. Furthermore, the optimum capacity additions including conventional generating units is obtained in the proposed model. The Benders decomposition approach is used for solving the optimization problem, including a master problem and two sub-problems with internal scenario analysis. In order to reduce the computational burden of the multi-year and multi-objective expansion planning problem, a multi-stage framework is presented in this paper. The uncertainties of wind speed and system demand along with contingency scenarios lead to a probabilistic optimization problem. Moreover, in the proposed model, the planning time horizon is divided into three predefined stages. This multi-stage approach is used to increase the proposed model accuracy in a power system with a high level of wind power penetration. Hence, in this paper a scenario-based probabilistic multistage model for transmission expansion planning is proposed, incorporating optimal WFs integration. It is recognized that high wind penetration increases the transmission expansion investment cost, but based on the reduction of the investment cost of conventional units, the total system cost will be smaller. This result emphasizes the main advantage of wind generating system over the conventional generating system. This planning methodology is applied to the modified IEEE 24-bus test system and simplified Iran 400-kV real system to show the feasibility of the proposed algorithm.

Abstract
Infrastructure-as-Code tools, such as Docker and Docker Compose, play a crucial role in the development and orchestration of cloud-native and at-scale software. However, as IaC relies mostly on the development of text-only specifications, these are prone to misconfigurations and hard to debug. Several works suggest the use of models as a way to abstract their complexity, and some point to the use of visual metaphors. Yet, few empirical studies exist in this domain. We propose a visual programming notation and environment for specifying Docker Compose configurations and proceed to empirically validate its merits when compared with the standard text-only specification. The goal of this work is to produce evidence of the impact that visual approaches may have on the development of IaC. We observe that the use of our solution reduced the development time and error proneness, primarily for configurations definition activities. We also observed a preference for the approach in terms of ease of use, a positive sentiment of its usefulness and intention to use.

Abstract
In recent years, mobile robots have become increasingly frequent in daily life applications, such as cleaning, surveillance, support for the elderly and people with disabilities, as well as hazardous activities. However, a big challenge arises when the robotic system must perform a fully autonomous mission. The main problems of autonomous missions include path planning, localisation, and mapping. Thus, this research proposes a hybrid methodology for mobile robots on an autonomous mission involving an offline approach that uses the Direct-DRRT* algorithm and the artificial potential fields algorithm as the online planner. The experimental design covers three scenarios with an increasing degree of accuracy in respect of the real world. Additionally, an extensive evaluation of the proposed methodology is reported.

Abstract
This paper presents the results of an Agent-Based Model developed to simulate the Iberian Electricity Market, with special focus on the modelling of hydro power plants. To simulate the agent's dynamics in the day-ahead market, it was developed a bidding strategy based on a Q-Learning procedure. In the computation area, the recent years brought the discussion around artificial intelligence to a new upper level to complement traditional models, driven by the increased hardware computer capabilities, as well as new developments in the machine learning area. Reinforcement Learning models, as Q-Learning, are being widely used to represent complex systems such as electricity markets. The developed model is designed to simulate in a detailed way the hydro units that have a large impact in the electricity market common to Portugal and Spain. Apart from describing the developed model, this paper also includes results from its application to the Iberian Market case along 2018. © 2020 IEEE.