Abstract
Supply chains are ubiquitous in any commercial delivery systems. The exchange of goods and services, from different supply points to distinct destinations scattered along a given geographical area, requires the management of stocks and vehicles fleets in order to minimize costs while maintaining good quality services. Even if the operating conditions remain constant over a given time horizon, managing a supply chain is a very complex task. Its complexity increases exponentially with both the number of network nodes and the dynamical operational changes. Moreover, the management system must be adaptive in order to easily cope with several disturbances such as machinery and vehicles breakdowns or changes in demand. This work proposes the use of a model predictive control paradigm in order to tackle the above referred issues. The obtained simulation results suggest that this strategy promotes an easy tasks rescheduling in case of disturbances or anticipated changes in operating conditions. © Springer International Publishing Switzerland 2017.

Abstract
In pharmacovigilance, reported cases are considered suspected adverse drug reactions (ADR). Health authorities have thus adopted structured causality assessment methods, allowing the evaluation of the likelihood that a medicine was the causal agent of an adverse reaction. The aim of this work was to develop and validate a new causality assessment support system used in a regional pharmacovigilance centre. A Bayesian network was developed, for which the structure was defined by an expert, aiming at implementing the current guidelines for causality assessment, while the parameters were learnt from 593 completely-filled ADR reports evaluated by the Portuguese Northern Pharmacovigilance Centre expert between 2000 and 2012. Precision, recall and time to causality assessment (TTA) was evaluated, according to the WHO causality assessment guidelines, in a retrospective cohort of 466 reports (April to September 2014) and a prospective cohort of 1041 reports (January to December 2015). Results show that the network was able to easily identify the higher levels of causality (recall above 80%), although strugling to assess reports with a lower level of causality. Nonetheless, the median (Q1:Q3) TTA was 4 (2:8) days using the network and 8 (5:14) days using global introspection, meaning the network allowed a faster time to assessment, which has a procedural deadline of 30 days, improving daily activities in the centre.

Abstract
This paper deals with integration of energy storage systems into electricity markets. We explain why the energy storage systems increase flexibility of both power systems and energy markets and why such flexibility is desirable, particularly when variable renewable energy sources are being used in existing power systems. As opposed to the existing literature, our model includes a dual technology energy storage system, acting in two different markets. We introduce a mathematical formulation for this model applied to two Dutch electricity markets. Adopting optimal control approach with the goal to maximize the yearly benefit, we show that the dual energy storage system can be profitable already when the same buying/selling strategies are adopted for the working days and weekends. We show that the profitability (slightly) increases with different buying/selling strategies for the weekdays and weekends. Finally, we demonstrate how the yearly benefit varies with size and efficiency of the devices chosen and market prices. (C) 2017 Published by Elsevier Ltd.

Abstract

Abstract
When public authorities face the need to improve a transportation system, they normally have to make a difficult choice among a set of technological and operational alternatives. To help the correct evaluation of each alternative and its impacts, costs and benefits, it would be useful to have a decision support tool based on approaches such as Multi-Criteria Decision Analysis (MCDA) and/or Cost-Benefit Analysis (CBA). Among the many impacts caused by a public transportation system, typically those on the land use are not adequately considered in the decision-making processes, mainly because they are hard to monetize, they are often considered as value transfer instead of value creation, and they are too complex to be assessed by traditional transport modelling tools. To overcome these weaknesses, the objectives of this research are to identify and measure the impacts of transit systems on land use and accessibility, and to consider those impacts in decision-making processes, along with more traditional financial and transport related impacts. For this purpose, a decision support tool, combining a land use and transport model with a MCDA model, was developed and assessed in a small case study. In future work, sensitivity and risk analysis will be incorporated, to more accurately and realistically reflect uncertainties and exogenous conditions that may significantly affect the costs and the benefits of a project. Finally, this decision support tool will be fully assessed in a study of the Green Line extension project in Boston, USA. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
This paper presents a study about the influence of Distributed Energy Resources' (DER) flexibility on the operation of a Medium Voltage (MV) network, in a Smart Grid (SG) environment. An AC multi-temporal Optimal Power Flow (OPF) tool was developed and used to simulate the impact of the DER flexibility (including storage devices, EVs, controllable loads and micro-generation) in distribution network operation. Some simulations are presented, demonstrating the impact that DER flexibility can have on solving operation problems namely in terms of branch loading and voltage limits.