Abstract
The adoption of serious games as a complement to traditional classroom training is still an emerging theme, but it offers relevant potentialities for both students and teachers. This study describes the integration process of serious games in an entrepreneurship course over five years (2014-2018). In the first three years, the ENTRExplorer was adopted, while in the last two years the FLIGBY was used. The experience of using entrepreneurship serious games is analyzed according to multiple perspectives, such as complexity, generation of more entrepreneurial or group working skills, engagement, interactivity, learning outcomes, or even the impact on the intention to establish a new venture. The findings allowed a comparative analysis of the two games, indicating significant differences in some of those dimensions. Nevertheless, the learning outcomes provided by each game were considered relevant by the students, showing that both games can be useful in the process of learning and acquiring entrepreneurship competencies.

Abstract
This paper addresses an extension of the flexible job shop scheduling problem by considering that jobs need to be moved around the shop-floor by a set of vehicles. Thus, this problem involves assigning each production operation to one of the alternative machines, finding the sequence of operations for each machine, assigning each transport task to one of the vehicles, and finding the sequence of transport tasks for each vehicle, simultaneously. Transportation is usually neglected in the literature and when considered, an unlimited number of vehicles is, typically, assumed. Here, we propose the first mixed integer linear programming model for this problem and show its efficiency at solving small-sized instances to optimality. In addition, and due to the NP-hard nature of the problem, we propose a local search based heuristic that the computational experiments show to be effective, efficient, and robust.

Abstract
This paper compares the accuracy of a set of 22 methods for short-term hotel demand forecasting for lead times up to 14 days ahead. Machine learning models are compared with methods ranging from seasonal naive to exponential smoothing methods for double seasonality. The machine learning methods considered include a new approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. Particularly, the dynamic ensemble method is used. The methods were compared using a real time series of daily demand for a four-star hotel located in the south of Europe. The forecasting performance of those methods was assessed using three alternative accuracy measures. Results from extensive empirical experiments led us to conclude that machine learning methods outperform traditional hotel demand forecasting methods. We found that the use of machine learning models can reduce the root mean squared error up to 54% for a 1-day forecast horizon, and up to 45% for a 14-days forecast horizon, when compared with traditional exponential smoothing methods.

Abstract
While it is generally accepted that our fossil fuel-dominated energy systems must undergo a sustainable transition, researchers have often neglected the potential impacts of this on water and land systems. However, if unintended environmental impacts from this process are to be avoided, understanding its implications for land use and water demand is of crucial importance. Moreover, developed countries may induce environmental stress beyond their own borders, for instance through extensive imports of bioenergy. In this paper, Germany serves as an example of a developed country with ambitious energy transformation targets. Results show that in particular, the politically-driven aspiration for more organic farming in Germany results in a higher import quota of biomass, especially biofuels. These imports translate into land demand, which will exceed the area available in Germany for bioenergy by a factor of 3-6.5 by 2050. As this will likely bring about land stress in the respective exporting countries, this effect of the German energy transformation ought to be limited as much as possible. In contrast, domestic water demand for the German energy system is expected to decrease by over 80% through 2050 due to declining numbers of fossil-fuelled power plants. However, possible future irrigation needs for bioenergy may reduce or even counterbalance this decreasing effect. In addition, energy policy targets specific to the transport sector show a high sensitivity to biomass imports. In particular, the sector-specific target for greenhouse gas reductions will seemingly promote biomass imports, leading to the above-described challenges in the pursuit of sustainability.

Abstract
Optimizing the operation of heating, ventilation, and air-conditioning (HVAC) systems is a challenging task that requires the modeling of complex nonlinear relationships among the HVAC load, indoor temperature, and outdoor environment. This article proposes a new strategy for optimal operation of an HVAC system in a commercial building. The system for indoor temperature control is divided into three sub-systems, each of which is modeled using an artificial neural network (ANN). The ANNs are then interconnected and integrated into an optimization problem for temperature set-point scheduling. The problem is reformulated to determine the optimal set-points using a deterministic search algorithm. After the optimal scheduling has been initiated, the ANNs undergo online learning repeatedly, mitigating overfitting. Case studies are conducted to analyze the performance of the proposed strategy, compared to strategies with a pre-determined temperature set-point, an ideal physics-based building model, and other types of machine learning-based modeling and scheduling methods. The case study results confirm that the proposed strategy is effective in terms of the HVAC energy cost, practical applicability, and training data requirements.

Abstract
Natural disasters such as earthquakes, hurricanes, and other extreme weather events along with human sabotage attacks pose serious risks to critical infrastructures especially electrical energy systems. Hardening and operational actions are the measures to improve the resiliency of the power systems against extreme events. The long-term hardening actions strive to organize the reinforcement of power system infrastructures which accomplished at the pre-events stage. Besides, the short-term operational measures such as network reconfiguration and generation scheduling are applied to form the multiple microgrids aimed at increasing the flexibility of the power system to cope with the severe events. These measures are taken during and after the occurrence of the disasters. In this paper, an integrated framework has been proposed to increase the resiliency of distribution system. In the proposed framework, there are two models so called defender-attacker-defender which are made to find the best possible solution in order to reduce the load-shedding of the system during extreme events. In the first model, the hardening measures are examined at the first level to increase the robustness of the system. The worst scenarios with the highest load-shedding are calculated in the second level and subsequently reconfiguration is performed in the third level to decrease the load-shedding. In the second model, the first and second levels specify the best reinforcement plan and the worst attack scenario respectively, and in the third level, optimal distributed generation placement is accomplished to supply the demand during islanding mode of microgrids. The proposed models are organized as tri-level mixed integer optimization problem and column constraint generation algorithm is utilized to make them computationally obedient. At the end, we have implemented the suggested models on the well-known IEEE 33-bus and 69-bus systems to prove their effectiveness and applicability at improving the resiliency of the distribution systems.