Abstract
The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R 2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.

Abstract
In this paper we present a Benders decomposition approach for the Berth Allocation Problem (BAP). Benders decomposition is a cutting plane method that has been widely used for solving large-scale mixed integer linear optimization problems. On the other hand, the Berth Allocation Problem is a NP-hard and large-scale problem that has been gaining relevance both from the practical and scientific points of view. In this work we address the discrete and dynamic version of the problem, and develop a new decomposition approach and apply it to a reformulation of the BAP based on the Heterogeneous Vehicle Routing Problem with Time Windows (HVRPTW) model. In a discrete and dynamic BAP each berth can moor one vessel at a time, and the vessels are not all available to moor at the beginning of the planning horizon (there is an availability time window). Computational tests are run to compare the proposed Benders Decomposition with a state-of-the-art commercial solver. © 2019, Springer Nature Switzerland AG.

Abstract
During the past few years, due to the growth of electric power consumption, generation costs as well as rises in the level of greenhouse gases efficiency bring special focus on distributed generation. Developing distributed generation resources, especially renewable energy resources, is one of the safest ways to solve such problem. These resources have been decentralised by being installed close to the houses producing few kilowatts. Therefore, there are no losses in transmission lines and provide response for demand. Based on their benefits, the use of such energy resources should be developed in the future, but its management and optimal use is a major challenge. This has become one of the main concerns ofenergy systems researchers. In the current study, an innovative model is provided as a strategic management. It is intended to optimise the operation in smart homes consisting of generation units such as a wind turbine, solar panels, storages, and un/controllable loads. The main objective of this optimisation management is to maximise microgrid profitability for 24 h. The overall results of the model proved that the profit of microgrid increased significantly.

Abstract

Abstract
Purpose The purpose of this paper is to identify patterns of project risk management (PRM) practices' adoption, and provides empirical evidence concerning the importance (and key attributes) of organizational PRM maturity to the use of risk-related practices and project performance. Design/methodology/approach The research involved two phases: interviews with five project managers, and a worldwide survey of project managers that resulted in the analysis of 865 valid questionnaire responses. Cluster analysis was used to classify PRM practices' use, factor analysis to detect the structure of the relationship between the variables measuring PRM practices' use and a multiple regression analysis (with canonical correlation) to further reveal the different degrees to which PRM practices and organizational maturity are associated. Findings The identified patterns of risk practices' adoption indicate that different contexts of organization PRM maturity and project complexity influence practices selection. The PRM practices related with targets (e.g. time-phased budget plan) are the most used, and those related to tools and techniques (e.g. S-curve) are the least used. Additionally, the obtained results confirm that organizational PRM maturity influences risk practices' usage, moderated by project complexity, and organizational PRM maturity influences project performance. Originality/value Empirical methods were used to investigate the relationship between organizational PRM maturity and a large set of PRM practices with project complexity as a moderator. Gaps in the use of PRM practices (i.e. areas where more PRM knowledge and training are needed) were identified. Finally, this work identifies the attributes of organizational maturity with implications in practices' usage and project performance.

Abstract
This article serves to show how things are changing when it comes to business and social networking. Nowadays, it is hard to find a business that does not have an account on any social network, and we can safely say that social media is a crucial aspect for any business enterprise - to sell their products, to be seen and, obviously, to make more money. We talk about the possibility to mix innovation, business strategy and social media. To complement this research, we focused our work on a Portuguese start-up - Strain - that intends to prove marketing is changing and that it actually welcomes the change, with social networks at the base of their business. They intend to use online influencers to promote the image of a brand, free of charge, and earn discounts on it. It is, indeed, a win-win situation, where each of the three parts (the company itself, clients and Strain) is a winner one way or the other.