Abstract

Abstract
The availability of patient clinical data can be vital to a more effective diagnosis and treatment, by an healthcare professional. This information should be accessible regardless of context, place, time or where it was collected. In order to share this type of data, many countries have initiated projects aiming to implement Electronic Health Record (EHR) systems. Throughout the years, some were more successful than others but all of them were complex and difficult to materialise. The research involves the study of four international projects - in Canada, Denmark, England and France - launched with the goal of fostering the clinical data sharing in the respective countries, namely by implementing EHR-like systems. Those case studies served as data to identify the critical issues in this area. To address the challenge of sharing clinical information, the authors believe to be necessary to act in three different dimensions of the problem: (1) the engagement of the stakeholders and the alignment of the system development with the business goals (2) the building of complex systems of systems with the capability to evolve and easily admit new peers (3) the interoperability between different systems which use different conventions and standards.

Abstract
Renewable technologies are suitable investments to achieve a low carbon electricity production system and to reduce the external energy dependency of Portugal in a long term period. The aim of this work was to develop and evaluate a variety of scenarios to promote these goals until 2030. A long-term electricity expansion planning model is used to design these scenarios and multi-criteria analysis is applied in the evaluation. The results demonstrated that imposing a minimum contribution of renewable energy sources (RES) for the electricity system, can be more costly than imposing CO2 emissions limitations. Taking into account the technical criteria, scenarios with high coal power share are favoured. However, under a pure social approach, the best scenario would be a 100% RES electricity system. When environmental and economic dimensions are more valued, the best options seems to be the ones with higher investments on natural gas and wind power plants.

Abstract
The two-dimensional cutting stock problem (2DCSP) consists in the minimization of the number of plates used to cut a set of items. In industry, typically, an instance of this problem is considered at the beginning of each planning time period, what may result in solutions of poor quality, that is, excessive waste, when a set of planning periods is considered. To deal with this issue, we consider an integrated problem, in which the 2DCSP is extended from the solution in only a single production planning period to a solution in a set of production planning periods. The main difference of the approach in this work and the ones in the literature is to allow sufficiently large residual plates (leftovers) to be stored and cut in a subsequent period of the planning horizon, which may further help in the minimization of the waste. We propose two integrated integer programming models to optimize the combined two-dimensional cutting stock and lot-sizing problems, minimizing the total cost, which includes material, waste and storage costs. Two heuristics based on the industrial practice to solve the problem were also presented. Computational results for the proposed models and for the heuristics are presented and discussed.

Abstract
We address two two-dimensional bin packing problems where the bins are rectangular and have the same size. The items are also rectangular and all of them must be packed with the objective of minimizing the number of bins. In the first problem, the two-stage problem, the items must be packed in levels. In the second problem, the restricted 3-stage problem, items can be grouped in stacks which are packed in levels. We propose a new decomposition model where subproblems are associated with the item that initializes a bin. The decomposition is solved by a heuristic which combines (perturbed) column generation, local search, beam branch-and-price, and the use of a general purpose mixed integer programming solver. This approach is closely related with SearchCol, a framework for solving integer programming / combinatorial optimization decomposition models. Computational results with 500 instances from the literature show that the proposed hybrid heuristic is efficient in obtaining high quality solutions. It uses more 8 and 17 bins than the 7364 and 7340 bins of a compact model from the literature for the 2 and 3-stage problems, respectively, while the sum of the time spent for all instances is 35% and 58% of the time spent by the compact model.

Abstract
To cope with today market challenges and guarantee adequate competitive performances, companies have been decreasing their products life cycles, as well as increasing the number of product varieties and respective services available on their portfolio. Consequently, it has been observed an increasing in complexity in all domains, from product and process development, factory and production planning to factory operation and management. This reality implies that organizations should be able to compile and analyze, in a more agile way, the immense quantity of data generated, as well as apply the suitable tools that, based on this knowledge, will supports stakeholders to take decision envisioning future performance scenarios. Aiming to pursuing this vision was developed a proactive performance management framework, composed by a performance thinking methodology and a performance estimation engine. While the methodology developed is an extension of the Systems Dynamics approach for complex systems' performance management, on the other hand, the performance estimation engine is an innovative IT solution responsible by capturing lagging indicators, as well as estimate future performance behaviors. As main outcome of this research work, it was demonstrated that following a systematic and formal approach, it is possible to identify the feedback loops and respective endogenous and exogenous variables responsible by hindering the systems behavior, in terms of a specific KPI. Moreover, based on this enhanced understanding about manufacturing systems behavior, it was proved to be possible to estimate with high levels of confidence not only the present but also future performance behavior. From the combination of both qualitative and quantitative approaches, it was explored an enhanced learning machine algorithm capable to specify the curve of behavior, characteristic from a specific manufacturing system, and thus estimate future behaviors based on a set of leading indicators. In order to achieve these objectives, both Neural Networks and Unscented Kalman Filter for nonlinear estimation were applied. Important results and conclusions were extracted from an application case performed within a real automotive plant, which demonstrated the feasibility of this research towards a more proactive management approach.